GEO Strategy for B2B and Enterprise Brands

The first call on your sales team's calendar usually opens the same way: the person on the other side is not waiting for you to introduce the company. They know the category's vocabulary, they say the names of two of your competitors out loud, they ask "I read that at our scale that module is licensed separately, is that right?" The first ten minutes of a thirty-minute meeting play out like the summary of a research process you were never part of.

None of this is new in B2B. That the buyer settles a significant part of the decision before ever speaking to a vendor is a finding category research has repeated for years. What has changed is where the research happens. It used to happen on a search results page, across a few comparison articles and one downloaded PDF, and at least it left a trace in analytics. Now part of that same work happens in a chat window: a question is asked, an answer is generated, the long list narrows. There is no vendor in that conversation, no log, no form.

This article treats GEO (Generative Engine Optimization) for enterprise and B2B brands not as a content calendar problem but as a problem of intervening in the part of the buying journey that has gone invisible. Let us say it at the outset: this channel measures poorly, its cycle is long, and internal approval processes are slow. All three have to be planned for deliberately.

The short list gets built without you hearing about it

The B2B buying journey has not moved into the assistant wholesale. The part that has moved is fairly specific, and it is exactly these four jobs:

Narrowing the long list. A question of the form "what are the maintenance management software options for a mid-sized manufacturing company." The model names five or six. A brand that is not on that list does not exist for that buyer on that day.

Learning the vocabulary. The person on the finance or operations side of the buying committee is new to the category. Instead of asking the vendor "what does this mean," they ask the assistant. The category's definition arrives from a source that is not your site.

"What is the difference between X and Y." This question used to send traffic to a comparison article. Now the answer itself is generated, and your page may or may not be in the source list.

"Is it a fit at our scale." The buyer types their own context: headcount, industry, current system, budget range. The model filters against those constraints. If the constraint information is not written plainly on your site, the model neither rules you out nor picks you, it skips you.

Once those four are done, the buyer is holding a short list of two or three names. The job is not over when your first-contact form is filled in, but the frame has long since been set. The moment you can intervene is not the moment the form is submitted; it is the moment the model decides which sources to read while answering those four questions.

Copilot on the corporate desktop: an internal question, an outward-facing answer

The channel that gets overlooked on the enterprise side is Microsoft Copilot. The reason is simple: the employee does not see it as a "search engine." They ask their question in the same window where they are drafting a proposal in Word, skimming a meeting summary in Teams, or replying to mail in Outlook. Part of the question draws on internal company documents, and part of it goes outside.

The technical distinction matters here. Copilot's answers that rest on internal data have nothing to do with you, you have no access to that data, and you should not have. But questions such as "who are the providers doing this in the market" or "which certifications does this standard require" go out to the web side, and there it is Microsoft's own search infrastructure that runs. The practical consequence: if you are not indexed in Bing, you are absent from that conversation on the corporate desktop.

Most teams have looked only at Google Search Console for years. If you do not have a verified property in Bing Webmaster Tools, you do not know what state your site is in on that side. The work is small and one-off: verify the property, submit the sitemap, check that robots.txt carries no block for bingbot, and list the important pages that are not indexed.

The second consequence sits on the content side: because these conversations happen inside a work context, the answers come back in a work context too. Not "which product is better" but "who does which step in this process." Content that explains a process and names roles and responsibilities is more useful here than a product page is.

What LinkedIn content is actually worth on the GEO side

Most of B2B marketing's content energy goes into LinkedIn. On the GEO side its value is narrower than assumed, and the reason is technical: a significant portion of LinkedIn content sits behind a login wall. A post can reach thousands of people inside the platform; that does not mean the text has entered the set of sources a language model reaches. How much of it the platform opens to crawlers is also a decision that can shift over time and is not under your control.

The rule that follows is practical: the canonical version of every idea you want to last should sit on your own domain. LinkedIn is a distribution channel, not an archive. If you wrote a long post, put an expanded version of the same content on the site and link to it from the post. The idea then circulates on the platform and also sits at an address you control and that is open to crawlers. Setting up and sustaining that distribution routine is part of social media management.

The split between a personal profile and a company page also works differently on the GEO side. Models treat people and organisations as separate entities; the link between the two is established only when it is repeated in public, consistent sources. If your expert's name appears only on a LinkedIn profile, that link stays weak. An author page on the site, a consistent byline across articles, the same title in talk and panel records: these firm up the entity side. That subject is a heading of its own, and we worked through it in detail in our guide to knowledge graphs and entity management.

Making thought leadership quotable

Most enterprise thought leadership is written in a way that makes it hard for a model to quote: long set-up paragraphs, a structure that reaches its conclusion on the third page, claims with no visible source. Rather than reading a piece end to end and summarising it, the model hunts for the fragment that carries the answer to the question. If that fragment is not sharp, it goes somewhere else.

The structure that works has three parts: claim, reasoning, limit.

• The claim is a single sentence and it is the first sentence of the paragraph. "In a long sales cycle the AI channel is defended with a short-list metric, not a first-contact metric."
• The reasoning is one or two sentences. It explains why this is so and by what mechanism it works.
• The limit comes at the end of the same paragraph. "This carries no statistical meaning in categories where volume is low." Writing the limit down does not weaken confidence; it stops the quote being used in the wrong context and makes the content auditable to a degree.

If you are going to use a number, keep its source inside the sentence: which institution, which year, which sample. Percentages with no source do damage twice over in enterprise content: they get stuck in legal review, and when they are quoted they cannot be verified.

There is a formatting rule as well: definition sentences should stand in a paragraph of their own. "ABM (account-based marketing) is the model in which marketing and sales work together against a predetermined list of accounts." Buried in the middle of another sentence, that sentence loses its quotability.

Turning the ABM list into a prompt set

Teams running ABM already hold something valuable: who the target accounts are, which industry they sit in, what size they are. On the GEO side, the way to turn that into measurement is to convert the account list into a pool of questions.

The method: pick four dimensions, and for each combination write the question the buyer would actually type.

Do not multiply all four dimensions out; picking the eight or ten cells your target accounts actually fall into and writing three or four questions for each is enough. The questions have to be in the buyer's language, not the vendor's. Not "best supply chain platform" but something closer to "what systems do people use to stop counting warehouse stock by hand at a 500-person food producer."

Keep that pool fixed and ask the same questions across several engines at regular intervals. What you record is simple: was your brand mentioned, was it shown as a source, which competitors came up, does the description in the answer match your positioning. Without a fixed question set, what you are measuring is not visibility but how lucky you got that day.

The attribution problem in a long sales cycle

Honesty is required here: if months, sometimes more than a year, sit between first contact and signature, the sentence "the AI channel produced this much revenue" cannot be constructed. If someone does construct it, it is invented. What can be done is to defend the channel not with a single metric but with several weak signals moving together.

The usable signals:

Referral traffic. Sessions arriving from assistant surfaces can be defined as a channel of their own. We set out the configuration step by step in our article on measuring AI traffic in GA4. The volume will be low, and that is normal.

Brand searches. A buyer who hears your name in an assistant most often turns back to a search engine and searches for your brand. The trend in brand queries can be tracked separately from the general trend inside the category.

A free-text source field. Add a free-text field for "how did you hear about us" to forms and first-call notes. Not a dropdown, free text. When a buyer writes "you came up when I asked ChatGPT," that single line is the most valuable data you have.

Short-list rate. Adding a "did we make the short list" field to opportunity records and comparing one period against another gives a much earlier and much more defensible signal than revenue attribution does.

None of these four proves causation. Moving together they can carry a budget conversation; taken one by one they cannot.

When a competitor comes up for "best X software" queries

Answers to category queries are usually assembled out of third-party content: comparison articles, industry listings, forum threads, user review sites. Your own product page is rarely the primary source for these queries. So the first job is not improving your own page, it is seeing which sources are being used. We laid out the method for that work in our guide to competitor analysis in AI search.

Your own comparison page can work too, but on one condition: that it is honest. A comparison table where you win on every row sends the reader and the model the same signal, that this is a marketing asset. A page that plainly writes down an area where the competitor is better looks instead like a measurable source of information.

The format that works in practice is this: on every comparison item, draw the "in which case us, in which case them" distinction. "In deployments that need a large number of custom integrations our model is the better fit; for single-location teams with standard processes the alternative goes live faster." That sentence also puts your sales team at ease, because it is what already gets said on the first call.

Queries of the "X alternatives" kind are a separate category. The buyer here is unhappy and looking for a way out. When you write that page, describe how the migration works instead of running the competitor down: data transfer, parallel running period, contract-end calendar. Content that lays the migration out concretely works more often on these queries than content full of praise does.

Legal and compliance sign-off: the real bottleneck

Most enterprise GEO work slows down not because of the quality of the content ideas but because of the approval chain. A blog post passes through legal, compliance, brand and sometimes product teams. That process can run for weeks, and the edits usually file down the most quotable part of the content: clear claims soften, the number is taken out, the definition sentence is generalised.

Rather than being annoyed by this, plan for it. Three methods work:

A pre-approved phrase library. Frequently used claims, certification wordings and capacity descriptions are approved once and go onto a list. If later content draws from that list, the approval round gets shorter.

A two-track calendar. Content that needs no approval (technical how-to, process explanation, glossary, FAQ) runs on a separate track from content that does (customer case, a claim containing a number, industry commentary). The first track never stops.

Bringing the approver in early. A plan shown to the legal side at the headline and claim-list stage, rather than after the draft is finished, reduces the large corrections that come later.

This slowness also raises a resourcing question: run the work inside, or bring in outside support. If the approval chain is complicated, an outside team cannot speed it up on its own; without an owner inside, the process jams. We took the variables in that decision one at a time in our article on an in-house GEO team versus an agency.

One last piece of expectation setting: in enterprise B2B the effect of this work is not measured by the quarter. Starting to appear in category queries, having your content seep into third-party sources, and being noticed in sales conversations all take time. Setting your measurement set up at the start and being patient is cheaper than expecting results in three months and giving up.

If you are not sure where to start on the enterprise side, get in touch and we will review your current content and visibility position together.

What our scope of work covers on the enterprise side is described on our B2B and enterprise brands solution page.

Frequently Asked Questions

What is the difference between GEO and SEO in B2B?

SEO aims to bring the buyer to your site by way of a search results page; GEO aims to have your brand mentioned and shown as a source inside the answer an AI assistant generates. In B2B that distinction matters especially, because when the long-list narrowing and vocabulary-learning steps happen inside an assistant, the buyer can assemble a short list without ever visiting your site. The two disciplines do not replace one another: technical accessibility, content depth and entity consistency feed both at once.

Does Microsoft Copilot really play a role in B2B purchasing?

Because Copilot sits inside the Office and Teams window an enterprise employee already has open, even questions that carry no "go and research this" intent can turn into vendor research. Answers resting on internal company documents are outside your reach; the answers to market, provider and standards questions, however, are fed from the web side, and there Microsoft's search infrastructure is what runs. The practical translation is this: a site that is not indexed on the Bing side is invisible in that conversation on the corporate desktop.

Does the content I publish on LinkedIn reach AI models?

Partly, and unreliably. A significant portion of LinkedIn content sits behind a login wall, and a post that got high reach inside the platform does not mean that text entered the sources models reach. So publish the canonical version of every idea you want to last on your own domain and use LinkedIn as a distribution channel; that way you get both the reach on the platform and an address you control that is open to crawlers.

How do you defend the AI channel's contribution in a long sales cycle?

If months sit between first contact and signature it cannot be defended with a single revenue figure, and if someone claims it can, that number is most likely invented. What you look at instead is several weak signals moving together: referral traffic from assistant surfaces, the trend in brand searches, free-text source statements in forms and call notes, and the short-list rate in opportunity records. None of them proves causation on its own, but when they move in the same direction they form a picture that can carry a budget conversation.

Is it worth writing our own comparison page?

It is, but only when it is honest. A table where you win on every row signals to the reader and to the model alike that this is a marketing asset, and it lowers the chance of being picked as a source in category queries. A page that plainly states the case where the competitor is better, and makes the "in which case us, in which case them" distinction explicit, looks instead like a measurable source of information; it also puts into writing the sentences your sales team already says on the first call.