B2B companies - how are you approaching AI search optimization? Traditional SEO playbook doesn't seem to apply

You’re experiencing the classic disconnect. High Google rankings don’t automatically translate to AI citations.

The fundamental shift:

The B2B AI optimization framework:

1. Answer Engine Optimization (AEO) Structure content to directly answer questions AI users ask.

2. Generative Engine Optimization (GEO) Ensure content can be parsed, extracted, and cited by AI systems.

Key tactics:

• Lead sections with direct 40-60 word answers
• Use question-based headers
• Implement comprehensive schema markup
• Create topic clusters demonstrating expertise
• Update content regularly with fresh data

The mindset shift: You’re not optimizing to rank - you’re optimizing to be selected as a trustworthy source when AI generates answers.

Here’s what we’ve implemented at our B2B SaaS company:

Content structure that works:

H1: [Specific Question Users Ask]

Opening paragraph (40-60 words):
Direct answer to the question. This is what AI extracts.

H2: Key Point 1 (Question format)
  Direct answer paragraph
  Supporting data table

H2: Key Point 2 (Question format)
  Direct answer paragraph
  Bullet list of specifics

FAQ Section (with schema)
  Q: Common follow-up question?
  A: Direct answer (40-60 words)

Why this works:

• Each section is a potential extraction chunk
• Question headers match natural queries
• FAQ section captures long-tail variations
• Tables and bullets are easily extractable

Our results: After restructuring 50 pages this way:

• AI citations up 340%
• Featured snippet wins up 180%
• Organic traffic also improved (bonus)

The structure helps both AI and traditional search.

Schema markup is critical for B2B AI visibility:

Priority schema types for B2B:

Implementation priorities:

1. FAQPage schema on all relevant pages
2. Article schema with author info on blog content
3. Organization schema site-wide
4. Product/SoftwareApplication schema on product pages
5. HowTo schema for instructional content

The structured data advantage: AI systems can extract information more confidently from structured data. You’re essentially pre-parsing your content for AI consumption.

Common mistake: Adding schema but not matching it to content. Schema should accurately reflect what’s on the page - misleading schema can hurt you.

Great questions. Here’s how we measure and what we’ve seen:

Measurement approach:

1. Am I Cited monitoring - Tracks brand/URL mentions across AI platforms. Baseline measurement before changes, ongoing tracking after.

2. Manual testing - Weekly testing of 50 target queries across ChatGPT, Perplexity, Google AI. Document citation appearances.

3. Referral tracking - Monitor AI platform referrals in analytics (though attribution is imperfect).

Business impact data:

Key insight: AI referrals convert at higher rates than organic search. Users who come from AI citations are further along in their decision process - the AI has pre-qualified them.

The ROI calculation: For us, one MQL = ~$200 in marketing cost. The AI optimization project cost ~$50K. At 73 additional AI-attributed MQLs/month, payback was under 4 months.

Adding the strategic layer:

B2B-specific considerations:

1. Buyer journey mapping for AI B2B buyers use AI throughout the funnel:

• Research stage: “What is [solution category]?”
• Comparison stage: “Best [solution] for [use case]”
• Evaluation stage: “[Product A] vs [Product B]”

Create content for each stage that AI can cite.

2. Multi-stakeholder optimization Different personas ask different questions. Your content needs to answer:

• Technical buyer: Architecture, integration questions
• Business buyer: ROI, case study questions
• Economic buyer: Pricing, TCO questions

3. Ungating strategy AI can’t access gated content. Consider:

• Ungating educational content
• Keeping only bottom-funnel assets gated
• Creating citation-optimized summaries of gated content

4. Expert positioning B2B decisions involve trust. Build:

• Thought leadership content with named experts
• Case studies with specific results
• Original research AI can cite

The shift: B2B content isn’t just lead gen fuel - it’s AI citation fuel that influences decisions before prospects ever reach your funnel.

On the question-based content approach:

Finding questions your buyers ask AI:

Research tools:

• Google’s People Also Ask
• AnswerThePublic
• AlsoAsked
• Semrush Question Hub

Sales team intelligence:

• Questions from discovery calls
• Common objections (frame as questions)
• Competitor comparison queries

Support/CS intelligence:

• FAQ patterns from customers
• Onboarding questions
• Use case clarifications

Question prioritization:

1. High purchase intent (“How to choose [solution]”)
2. Category questions (“What is [solution type]”)
3. Comparison queries ("[You] vs [Competitor]")
4. Use case specifics (“How to [achieve outcome] with [solution type]”)

Content mapping: Each priority question = dedicated content piece optimized for AI citation.

The insight: B2B queries are often more specific and technical than B2C. Your content needs to match that specificity to get cited.

This is the tension every B2B marketer faces. Here’s our framework:

Content ungating framework for AI:

Ungate completely:

• Educational/awareness content
• How-to guides
• Glossary/definitional content
• Blog posts and thought leadership

Create ungated summaries:

• Executive summaries of reports
• Key findings from research
• Top-level insights from guides

Keep gated:

• Full research reports
• Interactive tools/calculators
• Personalized assessments
• Bottom-funnel templates

The logic: Educational content drives AI citations which builds brand awareness and trust. Gated content captures prospects who are already interested.

The math: If ungating gets you cited in AI answers, you reach more prospects earlier. Even if conversion to lead is lower, total leads may increase because of larger top-of-funnel.

Our results: After strategic ungating:

• Total leads: Down 12%
• AI-attributed leads: Up 340%
• AI lead quality (SQL rate): 23% higher
• Net pipeline: Up 18%

The trade-off was worth it. Quality > quantity.

Technical SEO considerations for B2B AI visibility:

Core Web Vitals matter:

• LCP under 2.5s
• INP under 200ms
• CLS under 0.1

AI systems factor in page experience. Slow pages may not get crawled as thoroughly.

JavaScript handling: Many B2B sites are React/Angular with heavy JS. This is problematic:

• AI crawlers often can’t execute JS
• Content hidden until JS runs = invisible

Solutions:

• Server-side rendering (SSR)
• Static site generation (SSG)
• Pre-rendering for bot user-agents

Internal linking: AI discovers content through crawling. Strong internal linking from high-value pages helps.

Mobile-first: Many AI queries come from mobile. Ensure mobile experience is optimized.

Audit quarterly: Check for crawl errors, broken links, redirect chains. Technical issues = missed citation opportunities.

Incredible thread. Here’s our action plan:

Immediate (Month 1):

• Audit top 50 pages for AI optimization gaps
• Restructure 10 priority pages with question-based format
• Implement comprehensive schema markup
• Set up Am I Cited monitoring

Short-term (Month 2-3):

• Question research and content mapping
• Strategic ungating of educational content
• SSR implementation for JS-heavy pages
• Topic cluster development

Ongoing:

• Track AI citations weekly
• A/B test content structures
• Build thought leadership content
• Regular content freshness updates

Success metrics:

• AI citation frequency
• AI platform referral traffic
• AI-attributed MQLs
• Share of voice vs. competitors in AI

Thank you all for the detailed playbook!