AI SEO for B2B: how to position your brand in LLMs
As Sheridan Agency, we provide AI SEO services for B2B brands. On our own website we already rank for more than 14 keywords that trigger Google AI Overviews, including cases like Blaxtair. In B2B, this matters even more because visibility no longer happens only when someone searches, but also when AI systems “recommend” sources during the research phase.
What AI SEO is and why it matters more in B2B
AI SEO, also referred to as GEO or LLMO, means optimizing your digital presence for two scenarios at the same time:
- Keep winning rankings and clicks in traditional search.
In B2B, this is still critical to capture long-tail demand (“software X for Y,” “how to reduce Z,” “alternatives to…”) and to feed sales with qualified traffic. - Be selected as a source when AI generates an answer.
This is where the game changes: in B2B, many buyers research before speaking with sales. If AI summarizes “best practices,” “vendors,” “buying criteria,” or “comparisons,” you want to be included as a credible reference. - Influence the buying committee without relying on a single click.
B2B is rarely an individual purchase. You have the user, the sponsor, IT, procurement… and each role asks different questions. AI SEO helps you show up at more points in the journey, from “what is” to “security,” “integrations,” and “ROI.”
What AI considers “citable content” in B2B
1) Clarity and extractable structure because B2B is researched in pieces
In B2B, content is consumed in scan mode: someone wants a quick answer, a criterion, a benchmark, a requirements list. That’s why structure wins.
- Definition and context upfront: if you don’t clearly state “what it is” and “who it’s for,” AI has less clean material to extract.
- H2/H3 as real buyer questions: “Which security requirements should we ask for?”, “How do we evaluate vendors?”, “Which metrics matter?”
- Lists and comparisons: they work especially well in B2B because the user is trying to decide, not be entertained.
- Self-contained blocks: each section should make sense on its own. This increases “citability.”
Key B2B idea: the content that travels best into AI Overviews often looks like a mini evaluation sheet—criteria, steps, requirements, common mistakes.
2) Credibility through E-E-A-T in B2B: proof, not claims
In B2B, credibility is not optional. Buyers are trying to reduce risk, and AI systems tend to prioritize signals that look like “trusted sources.”
- Cases and evidence: “we work with X industries,” “we achieved Y in SERPs,” “learnings after Z implementations.” In your case, the “14+ keywords with AI Overviews” signal strengthens authority because it is concrete.
- Authorship and accountability: team pages, methodology, contact information, and when relevant, experts by area (technical, content, strategy).
- Sources and precision: if you talk about security, use clear concepts (SSO, RBAC, audit logs, SOC 2, GDPR) without guessing.
- Entity consistency: same naming, same services, same categories. In B2B, a brand is “learned” through consistent repetition.
3) Dynamic content because in B2B “outdated” feels risky
In B2B services and software, outdated content is interpreted as lack of rigor.
- Visible update dates on strategic assets (guides, comparisons, buying criteria).
- Living FAQs: questions change as the market evolves (AI Overviews, SERP shifts, new tools).
- A “changes and learnings” section: “what changed this quarter” works well because it turns your website into a continuous reference.
How to write B2B content AI can use without sounding like a boring manual
1) Answer like a consultant, not like a generic blog
A pattern that works in B2B is: context → criteria → decision → next step.
- Context: why it matters
- Criteria: how to evaluate it
- Decision: what to choose based on scenarios
- Next step: how to implement it
This covers search intent and also produces reusable material for AI-generated answers.
2) Turn every URL into an evaluation asset
In B2B, the highest-converting content tends to be:
- practical guides
- honest comparisons
- “alternatives to…” pages
- buying criteria
- templates (even if you don’t include a checklist in the post, you can offer a downloadable)
For AI, these formats are gold because they are structured and easy to cite.
3) Address the topic by role because committees ask different questions
Example for AI SEO:
- Marketing: visibility, demand, branded search, AI Overviews
- Sales: objections, comparisons, materials for the sales cycle
- Leadership: risk, ROI, pipeline impact
- IT and Legal: compliance, privacy, control
If your content covers 2–3 of these roles, you gain B2B depth without inflating word count.
4) Work in clusters aligned to the buyer journey
In B2B, the ideal cluster often maps like this:
- Awareness: what it is and why it matters
- Consideration: how to do it, best practices, common mistakes
- Decision: vendors, alternatives, cases, pricing
- Retention: measurement, iteration, scaling
That map is exactly what helps AI perceive you as a topical authority.
Minimum technical foundations that make the difference in B2B
1) Schema and semantics so systems understand your entity and your content
In B2B, it helps to reinforce:
- Organization (brand plus consistent data)
- Article or BlogPosting (informational pieces)
- FAQPage if you have real FAQs
- BreadcrumbList for site architecture
It’s not “for schema’s sake.” It’s to reduce ambiguity and help AI understand what is what.
2) Site architecture and internal linking as your website’s mental model
In B2B, a chaotic website kills authority. What works:
- one pillar per topic (AI SEO)
- satellites per subtopic (AI Overviews, LLMO/GEO, measurement, cases)
- contextual internal links (not “click here”)
This guides crawling and users alike: less friction means more trust.
3) UX and performance because B2B buyers also bounce
Even if the buyer is “serious,” they leave if they don’t find answers fast. In B2B, time is expensive.
4) llms.txt as support, not a solution
It can add a small signal, but it does not replace useful content, structure, authority, or freshness. In B2B, if the fundamentals are missing, llms.txt won’t save you.
Authority and mentions because in B2B it’s what others say about you
This is the part you notice when a brand starts being cited.
1) PR and citable assets
In B2B, what gets cited most often includes:
- studies (owned or collaborative)
- benchmarks
- frameworks
- industry reports
- technical glossaries
- comparisons with transparent criteria
You don’t need a massive annual report. A well-executed quarterly mini-report already creates strong signals.
2) Cases and social proof without hype
AI and humans trust evidence:
- “14+ keywords with AI Overviews”
- “verticals where it works best”
- “content types that show up most in summaries”
- examples of pages without exposing sensitive data
3) Entity consistency as a core B2B advantage
The more consistent you are, the easier it is to “identify” you:
- fixed naming
- same value proposition
- same services
- strong team and case pages
How to measure AI SEO in B2B without guessing
1) Business indicators that the committee understands
In B2B, connect “AI visibility” to metrics leadership recognizes:
- presence in AI Overviews by keyword
- mentions and citations when available
- brand search lift (“I saw you recommended”)
- assisted leads, not only last-click
- pipeline impact when you can track it
2) A simple control method you can maintain
- define clusters by theme
- assign a keyword list per cluster
- review AI Overview presence on a regular cadence
- note which format appears (FAQ, list, comparison)
- iterate based on what works
This is especially useful in B2B because it helps justify decisions to leadership: “we’re winning presence through X type of content.”
Common B2B mistakes and how to avoid them with context
Nice-looking content that is useless for decision-making
Talking about AI without criteria, steps, or examples. Fix: add decisions (“if you are X, do Y”).
Not addressing committee objections
Marketing speaks, but IT, Legal, and Procurement can’t find answers. Fix: sections on security, integrations, governance, and measurement.
Biased or empty comparisons
In B2B it shows quickly. Fix: comparisons with transparent criteria.
No proof
Promising “you’ll appear in ChatGPT” with no evidence. Fix: cases, methodology, SERP data.
No maintenance
In AI topics, old content looks like incompetence. Fix: visible, regular updates.
Frecuently asked questions
