Two competitors in the same niche. Similar products, similar pricing, similar customer satisfaction. One gets recommended by AI platforms consistently. The other barely shows up. The difference? How their content is written and structured.
Content has always been a competitive advantage. But the rules for what "good content" means are shifting. Google rewarded keyword optimization and backlinks. AI rewards something different: clarity, depth, and extractability.
Here's what actually works for getting AI to recommend your brand, with concrete before-and-after examples.
Write for answers, not for rankings
Google content is built around keywords. You target a phrase, optimize for it, and try to rank. The content often exists to serve the algorithm first and the reader second.
AI content needs to work differently. When someone asks ChatGPT "What's the best invoicing tool for freelancers?", the AI isn't looking for keyword-optimized pages. It's looking for content that directly answers the question with authority and specificity.
This means your content should actually answer questions, not just target keywords that relate to questions. The distinction is subtle but critical.
Before (keyword-optimized): "Looking for the best invoicing software? Our top-rated invoicing platform helps businesses streamline their billing process with powerful features and integrations."
After (answer-optimized): "We built this invoicing tool specifically for freelancers who need to send professional invoices in under 2 minutes, track payments automatically, and handle multiple currencies without complexity."
The second version answers a specific question about a specific audience. AI can extract that and use it in a recommendation. The first version is marketing language that tells AI nothing useful about who should use this product.
Structure content for extraction
AI doesn't read your page top to bottom like a human might. It scans for structure and pulls information from the sections most relevant to the user's query. Making your content easy to extract directly improves your chances of being recommended.
Use clear, descriptive headings
Headings aren't just visual organization. They're signals that tell AI what each section contains.
Before: "Our Approach" / "Why Choose Us" / "Getting Started"
After: "How We Help SaaS Companies Reduce Churn" / "What Makes Our Approach Different from Traditional Consulting" / "Three Steps to Start Your Retention Audit"
The second set of headings tells AI exactly what's in each section. When a user asks about SaaS churn reduction, AI can quickly identify the relevant section and extract useful information.
Add FAQ sections
FAQ pages and sections are one of the most effective content formats for AI visibility. They create a direct mapping between common questions and your answers.
But the questions need to be real questions your audience asks, not manufactured keyword-targeted questions.
Weak FAQ: "What is project management software?" (too generic, anyone could answer this)
Strong FAQ: "Can I use your project management tool if my team is split across 4 time zones?" (specific, relevant, shows you understand the user's real concern)
When someone asks AI a question that matches your FAQ, you've created a natural path for AI to reference your content.
Use comparison tables and structured data
AI excels at processing structured information. If you have a comparison between your product and alternatives, a clear table with features, pricing, and use-case fit is far more extractable than a wall of prose.
Same goes for pricing pages. Clear plan names, prices, included features, and limitations in a structured format help AI accurately describe your offering. Vague "contact us for pricing" pages give AI nothing to work with.
Go deep on your core topics
Surface-level content doesn't build the authority signal AI needs to recommend you. A 300-word overview of a topic tells AI you're aware the topic exists. A 1,500-word deep dive with examples, frameworks, and nuanced takes tells AI you're an authority.
This doesn't mean everything needs to be long. It means your core topics, the ones directly connected to what you sell, deserve thorough treatment.
Surface-level: "Email marketing is important for SaaS companies. Here are 5 tips to improve your email campaigns."
Deep: "After managing email sequences for 40+ SaaS companies, here's the exact framework we use for trial-to-paid conversion emails. Step one is the activation email, sent 2 hours after signup, focused on getting the user to complete one core action..."
The deep version demonstrates genuine expertise. AI picks up on this and is more likely to recommend you for related queries.
Write in a way AI can summarize
When AI recommends your brand, it typically provides a brief description. That description is synthesized from your content. If your content is easy to summarize, the AI description will be accurate and compelling. If your content is vague or convoluted, the AI description will be too.
Test this yourself. Can you read your homepage and, in one sentence, explain what your business does and who it's for? If you can, AI probably can too. If you can't, your messaging needs work.
Hard to summarize: "We leverage cutting-edge technology and industry-leading expertise to deliver transformative results that drive meaningful business outcomes for forward-thinking organizations."
Easy to summarize: "We help e-commerce brands reduce cart abandonment by optimizing their checkout flow. Average client sees a 15% improvement in 90 days."
The second version gives AI a clear, concrete summary to use in recommendations. The first version gives it nothing.
Create "recommendable" content types
Some content formats are inherently more useful for AI recommendations than others.
How-to guides that solve specific problems position you as an expert worth recommending. When someone asks AI how to do something, and your guide is the authoritative answer, you become the natural recommendation.
Case studies with specific results give AI concrete proof points to cite. "Increased conversion by 23% for a B2B SaaS client" is something AI can reference in a recommendation.
Comparison and evaluation content helps AI understand where you fit in the market. If you honestly compare your solution to alternatives and clearly state who should choose you (and who shouldn't), AI can use that information to make appropriate recommendations.
Definitive guides on your core topic establish topical authority. If you've written the most comprehensive resource on your specialty, AI is more likely to view you as the authority worth recommending.
What AI ignores
Just as important as what works is what doesn't.
Keyword-stuffed content that reads unnaturally. AI understands meaning and can see through forced keyword insertion.
Thin content that says nothing substantive. A page with a headline, two sentences, and a call-to-action gives AI no material to work with.
Pure sales copy without substance. "Buy now! Limited time offer! Don't miss out!" tells AI nothing about your expertise or value.
Gated content that AI can't access. If your best content is behind a lead form, AI can't read it and can't use it to inform recommendations. Consider whether at least a summary or key insights should be publicly accessible.
Putting it into practice
You don't need to rewrite your entire website tomorrow. Start with your most important pages: your homepage, your main service or product page, and your top 3-5 content pieces.
For each one, ask: Is the positioning clear? Can AI extract who this is for and what problem it solves? Is there depth, or is it surface-level? Are there structural elements (headings, FAQs, tables) that make extraction easy?
Make improvements iteratively. Track your AI visibility to see if changes move the needle. Tools like Mentionable can show you which prompts you appear for and which you don't, giving you a feedback loop for your content efforts.
The brands that write for AI don't sacrifice quality for humans. The same clarity, depth, and structure that helps AI also makes content more useful for real readers. It's not a trade-off. It's an upgrade.
