There's a huge difference between being mentioned by AI and being recommended by AI.
Mentioned: "Some options include X, Y, and Z."
Recommended: "I'd suggest X because it's particularly good for..."
One is a list item. The other is an endorsement.
This guide is about earning the endorsement.
What makes AI recommend vs. just list
AI tools decide what to recommend based on four things:
Relevance match: How well does this option fit what the user specifically asked for?
Authority signals: Is this a credible, trustworthy option?
Evidence strength: What supports this as a good recommendation?
Specificity: Is there a clear reason to recommend this for this particular use case?
To be recommended, you need all four. Miss one, and you're just another item in a list.
1. Nail the relevance match
AI recommends options that specifically match user needs. Generic doesn't win.
Define your positioning with precision
Who are you for? What problem do you solve?
Vague: "We help businesses grow."
Clear: "We're a CRM for freelance consultants who need to track both clients and projects."
When someone asks "what CRM should a freelance consultant use?", the clear positioning directly matches. The vague positioning could be anything.
Create content for specific use cases
Don't rely only on generic product pages. Create content for:
- "[Your category] for [specific audience]"
- "[Your category] for [specific use case]"
- "Best [category] for [specific need]"
Each piece of content makes you a strong match for that specific query.
Address the context users bring
If users ask about budget constraints, you need content addressing pricing. If they ask about integrations, you need content on that.
Match the contexts users bring to their questions.
2. Build authority AI can recognize
AI recommends brands it trusts. Trust comes from signals.
Third-party validation
- Reviews on trusted sites (G2, Capterra, industry-specific platforms)
- Press coverage in reputable publications
- Industry awards and recognition
- Expert endorsements
This validation gives AI confidence to recommend you.
Consistent expertise
- Comprehensive content in your domain
- Thought leadership (original research, unique insights)
- Active presence in industry conversations
Be the obvious expert in your space.
Social proof
- Customer case studies
- User testimonials
- Usage statistics
Evidence that real people trust you helps AI trust you.
3. Provide clear evidence
Recommendations need reasons. Give AI concrete evidence to cite.
Be specific
Instead of: "Our CRM is user-friendly."
Try: "Setup takes 15 minutes. No technical knowledge required. 94% of users rate onboarding as 'easy' or 'very easy.'"
Specific claims are citable. Vague claims aren't.
Include comparisons
"Unlike [alternative approach], our platform [specific advantage] because [specific reason]."
Comparison content gives AI material for "I recommend X because..."
Provide data
- Performance metrics
- Customer satisfaction scores
- Usage statistics
- Comparative benchmarks
Data makes recommendations concrete.
4. Stand out for something
Generic options get listed. Distinctive options get recommended.
Find your unique angle
What do you do that others don't? What makes you specifically good for certain users?
- "Best for teams under 10 people"
- "Only option with native [feature]"
- "Specifically designed for [industry]"
Own a niche
It's better to be the clear recommendation for a specific audience than a generic option for everyone.
"Best CRM for consultants" beats "One of many CRM options."
Be memorable
Clear positioning that sticks:
- "The anti-Salesforce CRM"
- "Email marketing that doesn't need a manual"
- "[X] for people who hate [typical pain point]"
5. Make it easy for AI
Even if you deserve to be recommended, AI needs to find and process the supporting information.
Structure for extraction
- Clear product descriptions
- Specific feature lists
- Explicit "who this is for" sections
- FAQ content
Create content that matches recommendation queries
- "Why [your brand] for [use case]"
- "[Your brand] is best for [specific type of user]"
- "Reasons to choose [your brand]"
Keep information current
Outdated information leads to less confident recommendations. Update regularly.
What to avoid
Vague positioning. "We're a leading provider of innovative solutions" tells AI nothing useful.
Generic content. If you could swap your brand name with a competitor's and the content still works, it's too generic.
Pure promotion. "We're the best!" without evidence doesn't lead to recommendations.
Trying to be everything. Trying to serve everyone means you're the specific recommendation for no one.
How to measure recommendation success
Track not just mentions, but:
Recommendation rate: When mentioned, are you positioned as a recommendation?
Recommendation context: What reasons does AI give for recommending you?
Recommendation position: Are you the primary recommendation or an afterthought?
Mentionable tracks these nuances across AI platforms.
The compound effect of being recommended
Being recommended creates momentum:
- AI recommends you
- Users try your product
- Users create content (reviews, mentions, discussions)
- That content reinforces AI's trust
- AI recommends you more confidently
- Repeat
The businesses earning AI recommendations now are building a flywheel competitors will struggle to match.
Your next steps
- Audit current mentions: Are you listed or recommended?
- Clarify positioning: Who specifically are you for?
- Build evidence: What data supports recommending you?
- Create specific content: Match target recommendation queries
- Build authority: Earn third-party validation
- Track and iterate: Monitor recommendation rates over time
Start with clarity about who you're for and why. Everything else builds on that foundation.