B2B buyers are researching differently in 2026. Before they open a browser tab for your competitor comparison or request a proposal, a significant and growing proportion are asking AI assistants: "What are the best [category] platforms for a mid-size manufacturing business?" or "Which UK agencies specialise in technical SEO for SaaS companies?" The AI response those buyers receive shapes their vendor shortlist before any traditional search or sales interaction occurs. Generative Engine Optimisation (GEO) for B2B is the discipline of ensuring your brand is present in those AI-generated responses rather than absent from them.
Why Is GEO Particularly Important for B2B Brands?
B2B purchase decisions are high-consideration and involve multiple stakeholders. The research phase can span weeks or months and typically involves multiple AI and search touchpoints before any vendor contact. A B2B brand that appears in AI-generated research phases at multiple points builds familiarity and credibility that a brand appearing only at the final evaluation stage cannot match.
The queries that B2B buyers use with AI assistants are often very specific: "best CRM for financial services firms under 500 employees," "UK payroll software integrating with Sage," "alternatives to [specific enterprise tool]." These queries are too specific for many brands to rank for in traditional organic search, yet AI assistants synthesise answers to exactly these queries from training data and live web searches. GEO targets this layer of search behaviour that traditional SEO does not fully capture.
What AI Systems Are B2B Buyers Using?
The primary AI channels for B2B research in 2026 are:
- ChatGPT (with web search): The most widely adopted AI assistant among business professionals globally. ChatGPT with the web search plugin retrieves live information and cites sources.
- Google AI Mode / Gemini: Integrated into Google Search and Google Workspace. B2B buyers using Gmail and Google Docs encounter Gemini throughout their workflow.
- Claude (Anthropic): Widely adopted in enterprise contexts via API integrations, Amazon Bedrock, and direct subscription. Strong in technical and analytical domains.
- Perplexity: A research-focused AI with transparent citation of sources, popular with technically sophisticated B2B buyers.
- Microsoft Copilot: Integrated into Microsoft 365, making it the default AI for organisations using the Microsoft stack.
Each system draws from different data sources and weights signals differently, but the common factors are: domain authority, content quality, factual specificity, named author credentials, and frequency of citation across the broader web.
What Content Signals Drive B2B GEO Citations?
Definitive Guides on Your Category's Key Questions
AI systems cite sources that comprehensively address specific business questions. For a B2B brand, this means publishing the most thorough available guide on the questions your buyers research before purchasing in your category.
A financial software company targeting CFOs should have detailed content answering: "What should a CFO look for in accounting software?", "How do you evaluate an ERP system?", "What are the key differences between management accounts and financial accounts?" These are not product pages; they are educational resources that signal expertise to AI systems.
The content must be genuinely comprehensive: covering the question from multiple angles, including counterpoints, acknowledging trade-offs, and providing specific actionable guidance. AI systems are trained on the premise that depth signals expertise; shallow coverage is not cited.
Case Studies with Specific, Quantified Outcomes
B2B case studies are powerful GEO assets when they include specific, verifiable outcomes. "We helped a 200-person manufacturing firm reduce their finance team's reporting time by 40%, from 5 days per month to 3 days per month, by implementing automated reconciliation workflows" is citeable. "We helped clients save time" is not.
Published case studies with named clients (where consent exists), industry context, problem statement, solution approach, and quantified results give AI systems the specificity they need to cite your brand as a relevant example.
Original Research and Industry Data
AI systems preferentially cite original data because it is unique and verifiable. Publishing original research (a survey of UK finance directors, an analysis of recruitment trends from your platform data, a benchmark report from your client base) makes your brand a primary source rather than a secondary commentator.
Original research earns links, which improves domain authority, which improves AI citation frequency in a compounding cycle. A B2B brand that publishes one substantial original research report per year consistently builds citation authority faster than one that publishes only commentary.
Expert Commentary and Thought Leadership
AI systems trained on web data learn to associate specific human experts with specific domains of authority. A CFO commenting regularly in financial media, a CTO publishing analysis of enterprise security trends, or an HR director contributing to HR management publications becomes a recognised entity that AI systems associate with that domain.
For GEO purposes, your key team members should be publishing under their own names in venues that AI systems index and weight: LinkedIn (significant training data inclusion), industry publications, major business media, and peer-reviewed or professional body publications.
How Do You Build the Technical Foundation for B2B GEO?
Schema Markup for Organisation and Services
Implement Organisation schema on your website, including sameAs links to your LinkedIn company page, Wikidata entry (if applicable), Companies House registration, and industry association memberships. Add Service schema for each distinct service or product category.
For B2B software companies, SoftwareApplication schema communicates specific technical details (operating system, device support, pricing model) that AI systems can extract for product comparison queries.
llms.txt File
Place an llms.txt file at the root of your domain summarising your company, key offerings, and linking to your most important content. This is an emerging standard that helps AI crawlers understand which content to prioritise when indexing your site.
A B2B software company's llms.txt might include:
# CompanyName
[Company Name] is a UK-based [category] platform for [target market].
## Key Resources
- [Product Overview](https://yourdomain.co.uk/product): Overview of platform capabilities
- [Integrations Guide](https://yourdomain.co.uk/integrations): Full list of native integrations
- [Security Documentation](https://yourdomain.co.uk/security): Security certifications and data handling
- [Case Studies](https://yourdomain.co.uk/case-studies): Client results by industry
Allow AI Crawlers in Robots.txt
Verify that your robots.txt file does not block AI crawlers including ClaudeBot, GPTBot, PerplexityBot, and Google-Extended. A robots.txt that blocks these crawlers removes your content from the data sources that AI systems use to generate responses.
How Do You Track B2B GEO Performance?
GEO tracking for B2B is still evolving as a discipline. The most practical current approaches are:
- Prompt testing: Systematically ask relevant AI assistants the specific queries your buyers use and document whether and how your brand appears in responses. Track this monthly.
- Mention monitoring: Tools like Mention, Brand24, and Google Alerts monitor for brand citations. Increase in AI-attributed mentions indicates improving GEO visibility.
- Search Console AI traffic patterns: Monitor for organic traffic increases to informational content that coincide with AI Mode adoption in your target market.
Dynamically works with B2B brands to build GEO strategies that ensure your company is visible in AI-driven research phases, not just traditional search. Get in touch to discuss a GEO programme for your business.
