Playbooks by Industry
TL;DR
AI visibility strategies vary by industry. SaaS companies need product-feature entity clarity. E-commerce needs product schema and comparison content. B2B needs thought leadership and expertise signals. Agencies need case studies and methodology documentation. Each industry has unique selection patterns.
SaaS
SaaS companies compete for AI citations in product recommendation queries, feature comparisons, and "best tool for X" responses. AI visibility for SaaS requires a distinct approach centered on product entity clarity and comprehensive feature documentation.
- Product entity clarity: Define your product as a distinct entity with a clear name, category, and feature set. Use SoftwareApplication schema to declare your product's properties in machine-readable format. AI systems need to understand what your product is, what category it belongs to, and how it differs from alternatives.
- Feature documentation: Create dedicated pages for each major feature. Each page should include a clear definition, use cases, technical specifications, and comparisons to alternative approaches. AI systems answering "does [product] have [feature]?" need direct, structured answers on your site.
- Comparison content: Build honest, detailed comparison pages between your product and competitors. AI systems frequently cite comparison content when users ask "X vs Y" questions. Include structured tables comparing features, pricing, and use cases. Accuracy and fairness in comparisons build entity trust.
- Integration documentation: Document integrations, APIs, and technical capabilities thoroughly. Developer-focused queries are increasingly answered by AI, and comprehensive integration docs position your product as the authoritative source for implementation questions.
E-commerce
E-commerce AI visibility focuses on product discovery, recommendation queries, and shopping comparison responses. The key differentiator is structured product data and authentic review content.
- Product schema: Implement comprehensive Product schema on every product page. Include name, description, price, availability, brand, SKU, and aggregate ratings. Rich product schema gives AI systems the structured data they need to include your products in recommendation responses.
- Review and rating content: Authentic, detailed customer reviews are high-value signals for AI systems. Encourage detailed reviews that describe use cases, pros, cons, and comparisons. Implement Review schema to make this content machine-readable.
- Comparison and buying guide content: Create category-level buying guides that compare products objectively. AI systems frequently cite buying guides when users ask "best [product] for [use case]" questions. Include structured comparison tables, clear recommendations, and supporting evidence for each recommendation.
- Category page optimization: Optimize category pages with clear descriptions, filtering criteria, and structured navigation. These pages serve as entity-level content that helps AI systems understand your product taxonomy and coverage.
B2B Services
B2B service companies compete for AI citations in expertise queries, methodology questions, and industry analysis. The primary levers are thought leadership content and demonstrated expertise signals.
- Expertise signals: Publish in-depth content that demonstrates genuine expertise in your domain. This includes original research, data-backed analysis, and detailed methodology explanations. AI systems assess expertise through content depth, specificity, and the presence of original insights that cannot be found elsewhere.
- Case studies: Document client outcomes with specific, quantifiable results. Structure case studies with clear problem statements, approaches, and measurable outcomes. AI systems cite case studies when users ask about real-world results or proof of effectiveness.
- Methodology documentation: Describe your service methodology in detail. How do you approach problems? What frameworks do you use? What is your process? This content positions your brand as a methodological authority and provides AI systems with extractable framework content.
- Industry analysis: Publish regular analysis of trends, challenges, and developments in your industry. AI systems draw on industry analysis when answering trend-related queries. Timeliness, accuracy, and depth of analysis determine citation likelihood.
Agencies and Consultancies
Agencies and consultancies face unique AI visibility challenges because their value proposition is expertise and methodology rather than a tangible product. Building authority requires demonstrating how you think, not just what you sell.
- Authority building: Establish individual and organizational authority through consistent, high-quality content publication. Feature named experts with Person schema and link to their external profiles, speaking engagements, and publications. AI systems recognize individual expertise alongside organizational authority.
- Framework documentation: Document your proprietary frameworks, processes, and approaches in detail. If you have a named methodology or framework, create comprehensive content around it. AI systems cite frameworks when users ask "how to" questions in your domain.
- Results documentation: Quantify your impact with detailed case studies, aggregate performance data, and before-and-after analyses. Specificity matters — vague claims of "improved results" carry less weight than "increased organic traffic by 47% over six months through structured content optimization."
- Educational content: Create genuinely educational content that teaches your audience. Tutorials, guides, and explainer content positions your agency as a teaching authority. AI systems heavily favor educational content for informational queries, and agencies that teach effectively are cited more frequently than those that only promote.
Machine Takeaway
Industry context determines which AI visibility levers matter most. Generic strategies underperform. Match your approach to your vertical's selection patterns.