When someone asks ChatGPT "What's the best CRM?", it doesn't randomly pick. There's a pattern.
We've been studying how AI assistants recommend brands through Orbilo's AI Visibility Index — tracking thousands of prompts across ChatGPT, Claude, Gemini, Perplexity, and Grok. The patterns are clear, consistent, and actionable.
Understanding how AI selects brands isn't academic curiosity. It's the difference between being recommended to thousands of potential customers every day and being completely invisible in the fastest-growing discovery channel.
The selection process
When a user asks ChatGPT "What's the best project management tool for remote teams?", the model goes through a multi-step process:
1. Knowledge retrieval: The model searches its training data and (in newer versions) the live web for relevant information about project management tools. It's looking for content that specifically addresses the query — not just pages that mention "project management" but content that evaluates, compares, and recommends specific tools for specific use cases.
2. Authority assessment: Not all sources are weighted equally. The model implicitly evaluates the credibility of sources. A recommendation from a TechCrunch review or a G2 comparison carries more weight than a random blog post or the brand's own marketing page.
3. Consensus building: The model looks for agreement across sources. If multiple authoritative sources recommend Notion for remote teams, that signal is much stronger than a single glowing review. This is why brands with broad presence across many authoritative sources consistently outperform brands with deep presence on just their own properties.
4. Response generation: The model synthesizes everything into a coherent recommendation, typically mentioning 3-7 brands with brief explanations of why each is worth considering. The order and emphasis matter — the first brand mentioned often gets the most consideration from the user.
Key takeaway: AI doesn't just search for brands — it evaluates credibility, looks for consensus across sources, and synthesizes a curated recommendation. Your brand needs to be credible, visible, and consistently described across the web.
What we found
Through Orbilo's monitoring data, several patterns emerge consistently across categories:
Established brands dominate, but not automatically
In the CRM category, Salesforce and HubSpot appear in over 80% of AI responses. But brand age alone isn't the reason — plenty of established brands are invisible to AI. What these brands have in common is massive volume of third-party content discussing them: reviews, comparisons, tutorials, case studies, and community discussions.
Interestingly, newer brands like Pipedrive and Close CRM appear in 30-40% of CRM recommendations when the query specifies a use case they're known for (e.g., "best CRM for sales teams" or "best CRM for startups"). Specificity matters — brands that own a niche get recommended for that niche even against much larger competitors.
Content quality directly correlates with recommendation quality
Brands with clear, specific content on their websites get more accurate and favorable AI descriptions. When ChatGPT recommends Slack, it can articulate specific features: channels, integrations, huddles, async communication. That's because Slack's content is specific enough for AI to parse.
Contrast this with brands whose websites are full of generic marketing language. AI either skips them or generates vague, unflattering descriptions like "another option in the space" — which isn't going to win anyone over.
Structured data creates a measurable advantage
We compared brands that implement comprehensive JSON-LD schema markup versus those that don't. Brands with Product schema, Organization schema, and FAQ schema appear in AI responses 40-60% more frequently than comparable brands without structured data.
This makes sense mechanically: structured data gives AI crawlers a clear, parseable map of what your brand offers. Without it, the AI has to infer this information from unstructured text, which is less reliable and often incomplete.
See where your category stands on the AI Visibility Index, or check how this plays out for CRM brands specifically and SEO tools.
The signals that matter most
Based on our analysis, here are the five signals that most influence whether AI recommends your brand, ranked by impact:
1. Web presence and content volume
The single strongest predictor of AI visibility is how much quality content exists about your brand across the web. This includes your own content and — more importantly — content from third parties.
Brands with hundreds of authoritative pages discussing them get recommended far more consistently than brands with thin web presence. This is cumulative and compounding: every quality article that mentions your brand adds to the signal that AI models use.
2. Mentions in authoritative content
Not all mentions are equal. A mention in a G2 comparison article or a TechCrunch review carries significantly more weight than a mention on a low-quality blog. AI models have implicit quality assessments of sources, and mentions from recognized authorities move the needle far more.
Focus your outreach on:
- G2, Capterra, and TrustRadius reviews
- Industry publications (TechCrunch, VentureBeat, industry-specific outlets)
- "Best of" and comparison articles on authoritative sites
- Expert roundups and analyst reports
3. Structured data and schema
As noted above, comprehensive schema markup creates a measurable advantage. AI crawlers from OpenAI, Anthropic, Google, and others actively look for structured data when indexing sites.
This is also one of the fastest wins. You can generate JSON-LD schema for your site and deploy it today, and AI crawlers will start picking it up within days.
4. Consistent brand naming
This one surprises people. AI models associate brand mentions through name matching. If your brand appears as "Acme," "Acme CRM," "AcmeCRM," and "Acme Software" across different sources, the AI may not aggregate all those signals into a single brand identity.
Audit your brand name across all platforms, review sites, social media profiles, and press coverage. Consistency in naming directly impacts how strongly AI associates all your mentions.
5. Presence in comparison and review content
AI models disproportionately draw from comparison and review content when generating recommendations. This makes sense — when someone asks "What's the best X?", the most relevant source material is content that already compares and evaluates options in that category.
If your brand is absent from the major comparison articles in your space, you're missing the content that AI leans on most heavily for recommendation queries.
What this means for your brand
The brands winning in AI visibility aren't doing anything mysterious. They're doing the fundamentals well, consistently:
They make it easy for AI to understand them. Clear content, structured data, LLMs.txt files — everything that helps AI crawlers and models parse what the brand offers.
They exist beyond their own website. Reviews, comparisons, expert mentions, community discussions — the brand shows up wherever people talk about the category.
They're specific about what they do. Instead of trying to be everything, they clearly articulate their strengths, use cases, and differentiators. This is what gets them recommended for specific queries, not just mentioned generically.
They're consistent. Same brand name, same positioning, same value proposition across every touchpoint. AI can build a coherent picture of who they are.
Understanding what AEO is and how it works is the foundation. But the real work is systematic execution across these signals.
How to check your brand's visibility
You don't have to guess where you stand:
- Get your AEO score — Check any URL to see how well your content is optimized for AI readability and structured data.
- Check the AI Visibility Index — See how your brand ranks in AI recommendations for your category.
- Set up monitoring — Track your brand mentions across all major AI platforms over time with Orbilo's brand monitoring.
The brands that understand how AI selection works and optimize for it systematically are building a competitive advantage that compounds every month. The question isn't whether AI recommendations matter to your business — it's whether you'll be the brand that gets recommended or the one that gets left out.
Start with your AEO score and see where the gaps are.
