AEO and GEO for B2B: How AI Engines Choose Vendors

When a buyer asks AI which vendors fit their requirements, is your company on the shortlist—and is the explanation accurate? For a B2B marketing leader, that is the practical question behind answer engine optimization (AEO) and generative engine optimization (GEO).

You may have strong search rankings and useful content, yet an AI answer can overlook your company, describe an outdated capability, or recommend you for the wrong buying situation. Each finding calls for a different response. With a small marketing team, the priority is to identify the gap before committing to another content program.

Algorithm's recommendation: Start with five real buyer questions. Check whether you are recommended for the right use case, what evidence supports the answer, and which facts need correcting. Use those findings to decide where to invest across your website and the independent sources buyers consult. SEO remains the foundation.

Keep the stakes in perspective: An omission or inaccurate answer is a risk worth investigating. It does not, by itself, prove that you have lost leads or revenue.

Your first move: test five questions from a real buying decision

Ask sales for questions from recent qualified opportunities. Choose a product or service you want to grow, then cover these five decisions. Replace the bracketed terms with your buyers' language; these are illustrative templates, not measured search-demand claims.

1. Discovery: “Which [solution category] providers fit [operating situation] and [critical requirement]?”
2. Fit: “What should a company with [operating constraint] look for in a [solution category] provider?”
3. Comparison: “How do [your company] and [a relevant competitor] compare for [specific use case]?”
4. Implementation risk: “What integration, deployment, and support questions should we ask before buying [solution category]?”
5. Commercial evaluation: “What affects the total cost of [solution category], and what should procurement verify with vendors?”

Run the questions in two search-enabled AI tools relevant to your buyers, using fresh conversations. Save the date, exact prompt, tool and mode, full answer, and cited links. Repeat a surprising result before treating it as a pattern. Keep discovery and fit questions unbranded: naming your company in a comparison tests how it is described, not whether a buyer would discover it.

Turn each finding into a marketing decision

The useful output is a prioritized decision, not a visibility score alone. Choose the issue most likely to affect a qualified buyer's evaluation, assign an owner, and document what you change. This quick check sets priorities; it is not a representative measure of the market.

What AEO and GEO mean for B2B marketing

AEO is the broader term for improving visibility in systems that return direct answers. GEO usually refers to AI-generated responses, including answers in ChatGPT, Perplexity, Gemini, Microsoft Copilot, and Google's AI Overviews and AI Mode. In practice, the work behind the two overlaps: accessible pages, clear company facts, useful answers, and credible sources.

You do not need to split your strategy into separate SEO, AEO, and GEO publishing programs. Build one search plan around your buyers' decisions. Our AEO vs. SEO comparison explains what stays the same and what changes.

How an AI answer can turn into a vendor shortlist

When a search-enabled AI system answers a question, it can retrieve current pages and combine information from several sources. That retrieval is different from training a model. Not every answer uses a live search, and platforms do not all choose or cite sources the same way.

Google documents a technique called query fan-out: AI features may issue multiple related searches across subtopics and data sources to build a response. This is why optimizing only for a broad category term can miss useful opportunities. A buyer's question about warehouse automation may also require evidence about integration, layout constraints, maintenance, and deployment. Source: Google Search Central's AI features guidance.

Our recommendation: Map the questions inside the buying decision. Give each important question a useful answer, either on a relevant product or use-case page or in a linked supporting resource. Do not create a thin page for every wording variation.

Start with questions your sales team has to answer

These are illustrative prompts, not measured search-volume claims or examples of client results:

• “Which warehouse management systems suit a multi-site 3PL with customer-specific billing requirements?”
• “What should we compare when evaluating autonomous mobile robots for an existing warehouse with narrow aisles?”
• “Which fleet telematics providers support 500 vehicles, mixed vehicle types, and maintenance reporting?”

Then ask what a technical evaluator needs to verify, what an operations leader needs to run the solution, and what procurement needs to compare. For the warehouse management example, that means integration requirements, billing workflows, implementation responsibilities, support coverage, and cost assumptions.

A useful comparison explains who the solution fits, where it does not fit, what evidence supports the claim, and what remains to be checked. Those details help a buyer evaluate you even if an AI tool never cites the page.

Measure mentions, citations, and accuracy separately

At Algorithm Digital Marketing, we recommend keeping these three observations separate in a visibility baseline:

• Mention: Is your company named in the answer? Record whether it is recommended for the use case or merely listed.
• Citation: Does the answer link to your website, or to another source discussing your company? Save the URL and note which claim it supports.
• Accuracy: Are the category, capabilities, customer fit, geography, and limitations correct? Check them against approved company material.

A citation to a glossary page is different from a recommendation for your product. A competitor appearing more often in a small prompt sample does not establish market share. Treat a benchmark as a dated observation with a clear method.

A baseline you can build in a spreadsheet

1. Expand your initial five-question check to 20 to 30 questions from sales conversations, request-for-proposal documents you are allowed to use, customer interviews, and search data. Separate unbranded discovery questions from questions that name your company.
2. Test the same set in the platforms your buyers use. Record the date, platform, available model or mode, region, whether web search was enabled, exact prompt, answer, and cited URLs.
3. Use fresh conversations and consistent conditions to reduce context effects. Repeat important prompts to expose variation; a fresh conversation cannot remove every personalization effect.
4. Mark mentions, citations, and factual errors separately. Record the competitors and third-party sources that recur.
5. Re-run the set monthly using the same method. If a platform or prompt changes, annotate the change rather than presenting it as a clean comparison.

Keep the business outcome beside the visibility data. In GA4, review identifiable AI referral sources and their inquiries or other meaningful actions. Compare lead quality in your contact records, and consider asking “How did you first hear about us?” on forms or in discovery calls.

Referral reports count detectable visits, not every AI-influenced buying decision. A buyer can see your name in an answer, then return through Google or type your address directly. Google also includes AI Overviews and AI Mode traffic in Search Console's overall Web search reporting; that report does not provide a clean standalone AI visibility baseline. Source: Google's measurement guidance.

Fix access and evidence before producing more content

1. Check what each crawler control actually does

Google says a page must be indexed and eligible to show a snippet to appear as a supporting link in its AI features. There are no additional technical requirements or special AI schema. Check indexing, robots rules, snippet controls, internal links, and whether important text is available to crawlers.

• Googlebot: Controls crawling for Google Search, including its AI features. Google-Extended is a separate control for certain Gemini training and grounding uses; it does not control inclusion in Google Search or its ranking.
• OAI-SearchBot: OpenAI's search crawler. GPTBot is separately associated with model training. A company can make distinct decisions about search visibility and training access.
• PerplexityBot: Perplexity describes this crawler as supporting search results, rather than foundation-model training.

Check firewall and hosting rules as well as robots.txt. Verify official crawler documentation before changing access; allowing a bot does not guarantee a mention or citation. See Google's crawler documentation, OpenAI's bot documentation, and Perplexity's bot documentation.

2. Make the buying evidence easy to reach

Check key pages with JavaScript disabled and with your search inspection tools. Important product descriptions, fit criteria, and evidence should not depend on a click, login, or a fragile script. This is an accessibility and crawlability check, not a claim that every search system ignores JavaScript.

Refresh the existing pages buyers need most. Put the direct answer near the relevant heading, then explain assumptions, trade-offs, and proof. Date changing specifications and name the person responsible for reviewing technical claims. Keep confidential customer details private unless you have permission to publish them.

3. Check the sources outside your website

Compare approved company facts with your LinkedIn profile, product directories, distributor and partner pages, and review platforms. Correct outdated categories, unsupported capabilities, and old product information.

Use the sources cited in your own prompt baseline to choose where to invest. Depending on the category, useful places may include trade publications, relevant G2 or Capterra listings, partner resources, analyst coverage, and genuine industry discussions. These are places to investigate, not a universal list of sites AI engines prefer. Avoid manufactured reviews and promotional forum posts.

Three shortcuts that do not establish AI authority

• An llms.txt file is not an inclusion guarantee. Google's guidance says no new AI text files are required for its AI features. Do not make such a file the centerpiece of your search plan.
• FAQ or HowTo markup does not secure citations. Use structured data that accurately matches visible content. Google restricts FAQ rich results to well-known authoritative government and health sites and deprecated HowTo rich results. A useful FAQ still helps readers; its markup is not an AI ranking promise.
• Publishing AI-written pages at scale is not proof of expertise. The risk is producing unoriginal, unsupported content with little value. Google warns that generating many pages without adding value can violate its scaled content abuse policy. Using AI as an editorial aid is different from publishing unchecked output.

Sources: Google's AI features requirements, FAQ and HowTo changes, and guidance on generative AI content.

A 30/60/90-day plan for a B2B marketing team

This is a planning framework, not a forecast of rankings or citations. Assign an owner and agree on the buyer decision each task supports.

Days 1–30: establish the baseline

• Run the five-question check first, then expand the buyer-question set around your priority use cases. Save answers and sources.
• Check indexing and crawler access for priority pages.
• Review company facts with sales and product owners. Prioritize errors that could disqualify you or mislead a buyer.

Leave this phase with: a dated baseline, an error list, and an owned action plan.

Days 31–60: close the highest-value evidence gaps

• Improve the use-case, comparison, implementation, or specifications pages that answer your most important buying questions.
• Add verified evidence and explicit limitations. Link supporting pages from the main product or service page.
• Correct priority third-party listings and choose relevant editorial or partner opportunities.

Leave this phase with: stronger buying evidence and a record of what changed.

Days 61–90: retest and decide what deserves more investment

• Repeat the baseline and review errors, mentions, and citations under comparable conditions.
• Review identifiable referrals and inquiry quality with sales.
• Expand work where a buyer-relevant gap remains. If visibility has not changed, check retrieval, source selection, and factual relevance before increasing publishing volume.

Leave this phase with: a documented comparison and the next quarter's priorities. Technical fixes can be completed before they are reflected in answers; independent coverage and source updates may take longer. No reliable universal timeline guarantees AI visibility.

What should you do after reading this?

Take the five buyer questions above to your next sales conversation. Agree on the buying situation that matters most, run the check, and select one evidence or accuracy gap to address first. Assign an owner and a review date. You should leave with a clear investment decision—not an instruction to publish more content by default.

If you need an independent starting baseline, explore the AI Search Benchmark Report. For ongoing work across your pages and the sources buyers use, see AI search optimization for B2B companies.

Frequently asked questions

Do AEO and GEO replace SEO?

No. AEO and GEO build on accessible, relevant, trustworthy content. Google says its AI search features use the same foundational SEO practices, with no additional technical requirements. Keep SEO and AI visibility in one buyer-led search plan.

Why does a competitor get mentioned when our website ranks well?

An AI answer can draw on multiple pages and third-party sources to match a specific use case. Review the cited sources and the buying constraints in the question. Traditional rankings alone do not explain source selection, and one answer is not enough to establish a consistent pattern.

Can we block model training while allowing search visibility?

Some platforms provide separate controls. OpenAI distinguishes OAI-SearchBot for search from GPTBot for training. Google-Extended does not control inclusion or ranking in Google Search. Review the current documentation for each platform before changing robots or firewall rules.

How do we show the value when AI answers do not send clicks?

Report prompt-level mentions, citations, and accuracy alongside identifiable referrals and qualified inquiries. Ask buyers how they found you where appropriate. Be explicit that a prompt sample is not market share and that analytics cannot attribute every AI-influenced purchase.

How long does AEO or GEO take to work?

There is no universal or guaranteed timeline. Use the first 30 days to establish a baseline, the next 30 to improve priority evidence, and the next 30 to compare observations. Indexing, retrieval, platform changes, and third-party updates can affect when results appear.

About the author

Amanda Biddlestone founded Algorithm Digital Marketing in 2018 after 15 years working inside marketing departments. Her 20 years in B2B digital marketing include supply chain, logistics, industrial software, manufacturing, and robotics. Her in-house background includes Fleetmatics through its $2.4B acquisition by Verizon. Learn about Amanda and Algorithm Digital Marketing.