The way businesses evaluate and compare solutions has already shifted. Most companies just haven't restructured their content to match.
Two years ago, if a VP of Marketing wanted to compare HubSpot operations partners, they'd Google it. They'd click through five or six agency websites, skim some case studies, maybe read a blog post or two. The comparison happened in their head, across tabs, over the course of a few days.
Today, a growing number of those buyers start by asking an LLM. They open ChatGPT or Claude and type something like "what should I look for in a HubSpot operations agency" or "managed HubSpot services vs. hiring an in-house ops person" or "best tools for deal health scoring in HubSpot."
The LLM doesn't give them ten blue links. It gives them an answer. A synthesized, reasoned response that references whatever content it has access to, weighted by specificity, clarity, and relevance. The buyer reads the answer, maybe follows a link or two, and forms an opinion before they ever visit your website.
If your web presence is built for Google circa 2019, you're optimizing for a channel that's ceding ground to a fundamentally different one. And the content that wins in the new channel is not the content that won in the old one.
This matters because the mechanics of LLM responses are nothing like the mechanics of Google rankings.
Google rewards keyword density, backlinks, domain authority, and technical SEO signals. You can rank for "HubSpot operations agency" by hitting the right keyword targets with a reasonably structured page, even if the page itself is generic marketing copy that could belong to any of fifty agencies.
LLMs work differently. When someone asks a comparison question, the model synthesizes an answer from the content it's been trained on and (increasingly) from live web content it can access through search. It's looking for content that actually answers the question with specifics: what does this company do, how do they do it, what makes their approach different, what outcomes have they produced, and how do they compare to alternatives.
Generic service pages fail this test completely. "We help businesses unlock the full potential of HubSpot" tells the LLM nothing useful. It can't distinguish that from any other agency's page. There's nothing specific enough to cite when a buyer asks for a recommendation.
Detailed, opinionated, operationally specific content passes the test. A page that explains exactly what a HubSpot operations audit involves, names the five layers of assessment, describes what typically gets found, and gives the reader enough detail to understand the approach? The LLM can work with that. It has something concrete to reference when a buyer asks "what does a HubSpot audit look like?"
Forget the term "LLM optimization" for a second. What we're really talking about is content that is specific enough to be useful when a machine is trying to answer a question on your behalf.
A few things make the difference.
Start with positioning. If your website says "we're a full-service digital agency that does everything," the LLM has no way to recommend you for anything specific. Compare that to "we're an operations agency for companies already running on HubSpot, focused on CRM governance, pipeline architecture, and ongoing operational ownership." The LLM knows exactly when to bring you up because there's a clear description of who you are and what you do.
LLMs also anchor heavily on named methodologies. Describing your audit process as "comprehensive" is a dead end. Describing it as a five-layer assessment covering data architecture, automation logic, lifecycle integrity, reporting accuracy, and integration health gives the model structure it can reference and compare against other approaches.
Then there's comparison content. When someone asks "managed HubSpot services vs. hiring in-house," the LLM pulls from whatever addresses that question directly. If you've written a detailed post walking through the trade-offs, you're in the answer. If you haven't, a competitor is. The same holds for platform decisions like HubSpot vs. Salesforce, where buyers actively ask LLMs to weigh the options for them.
Specific outcomes matter more than you'd think. "We helped a client improve their operations" gives the LLM nothing to work with. "We reduced CRM data errors by 60% for a 50-person real estate investment firm by rebuilding their property architecture and automating data validation" gives it a concrete example it can cite. The difference is enormous.
And opinions differentiate in ways that neutral content can't. Content that says "there are pros and cons to both approaches" is less useful to the model than content that takes a clear position: "in our experience, companies under 200 employees almost always get more value from managed services than an in-house hire, and here's why."
Why traditional SEO content fails in LLM responses
Most B2B content is written for a Google-shaped funnel. Target a keyword, hit a word count, include the keyword in your headers, build some backlinks, rank.
The problem is that content built this way tends to be deliberately vague. It's optimized to attract clicks from a wide range of search queries, which means it avoids specifics that might narrow the audience. It says "best practices for HubSpot" instead of explaining an actual practice. It says "improve your CRM" instead of describing exactly what improvement looks like.
That vagueness is exactly what makes it invisible to LLMs. The model is looking for content that answers specific questions with specific answers. A 2,000-word blog post that circles around a topic without ever committing to a clear position is easy for Google to rank and almost useless for an LLM to cite.
The irony is that the content LLMs favor is content that many SEO strategies would reject for being too narrow or too opinionated. But when a buyer asks an LLM a specific question, the most specific answer wins.
Every blog post we've published at Fission over the past few months follows this pattern, and it wasn't accidental.
Our post on what a HubSpot operations audit actually uncovers doesn't say "we do audits." It walks through the five layers of assessment, describes what we typically find at each layer, explains why each finding matters, and connects the audit to a remediation roadmap. When someone asks an LLM "what does a HubSpot audit involve," that post has the specifics the model needs.
Our post on portal drift describes seven specific symptoms with enough operational detail that the LLM can match them against a buyer's description of their own situation. "My HubSpot workflows are a mess and nobody knows what they do" maps directly to content that names that exact problem.
Our post on deal health in HubSpot walks through what you can do natively, where the native capabilities stop, and what fills the gap. When someone asks "how do I score deal health in HubSpot," the LLM can reference the specific comparison between native tools and Data Parrot because the content is structured around that exact question.
Our post on why your CRM data is lying to you takes a specific position on why native HubSpot reporting can't be trusted at face value, and our post on HubSpot building AI on 280,000 portals engages directly with a current industry development and supports a clear argument with operational evidence. LLMs weight opinionated analysis from practitioners more heavily than neutral summaries.
None of these were written with "LLM optimization" as the primary goal. They were written to be genuinely useful to people evaluating whether they need help with their HubSpot operations. But that's the point. The content that's most useful to a human evaluating options is the same content that's most useful to an LLM synthesizing answers for that human.
And we can see it working. We track our own visibility in answer engines using a combination of AEO tracking tools and direct query testing, and since we started publishing content structured this way, we're now being surfaced in AI answers for questions we specifically wrote content to address. To be straight about it: answer engine optimization is new enough that there isn't a deep body of research proving long-term impact, and anyone claiming precise ROI figures is guessing. What we can say is that the content that performs in answer engines is the same content that builds trust with human readers, so there's no real downside to structuring it this way. The upside is a growing channel. The downside is close to zero.
If you want the tactical version, this is the abbreviated version of the SOP we follow. You can also see the finished product: our own reference file, linked at the bottom of this post.
Lead with a direct, specific answer to the question the page is targeting. Don't bury it under 400 words of preamble. Answer engines extract the most direct answer they can find, as early as they can find it.
Name your methodology, framework, or process explicitly. "Five-layer audit" beats "comprehensive audit" every time because the model has something concrete to hold onto and cite.
Structure content so each section answers one specific question. Clear headers that map to real questions people ask. The more your structure mirrors the shape of a question, the easier it is for a model to pull the relevant part.
Include concrete specifics: numbers, named categories, real examples, defined terms. Vague claims get skipped. Specific claims get cited.
Take a clear position. Answer engines weight sources that commit to a point of view over sources that hedge, because a position helps the model give a better answer.
Cross-link related content so the model can see the full shape of your expertise across multiple pages, not just one.
That's the core of it. Simple to describe, harder to do consistently, which is exactly why most content doesn't do it.
If you're a B2B company and your web presence is mostly generic service pages with a blog full of keyword-stuffed SEO posts, you have a visibility problem that's going to get worse.
The shift toward LLM-mediated discovery doesn't mean Google is dead. It means there's a second channel now, and it rewards different content. Google rewards reach. LLMs reward specificity. You need both.
Practically, that means a few things.
Service pages need to explain what you actually do with enough detail that a machine could describe your approach to someone who asked. "Full-service HubSpot agency" is invisible. "Operations agency focused on CRM governance, pipeline architecture, and ongoing managed services for companies already running HubSpot" is findable.
Your blog needs to answer the specific questions your buyers are asking, in your voice, with real answers. "Here's what we find in every audit, here's why it matters, and here's what we do about it" is the kind of content that shows up in LLM responses. Generic tip lists don't.
Comparison content needs to exist. If someone asks "managed services vs. in-house" and you haven't published your take on that, you're absent from the answer. Someone else fills the gap.
And content needs opinions. LLMs can summarize neutral information from anywhere. They cite and recommend sources that take positions, because positions help them give better answers to people asking for guidance.
The best content for LLMs is the same content that builds trust with humans. Content written by people who actually do the work, grounded in real outcomes, with a clear point of view.
The companies that invest in that now will show up in answers when their next buyer asks an LLM who to hire. The ones that don't are building a visibility gap that gets harder to close every quarter. This channel is early, the research is thin, and nobody can promise you a precise return. But the content that wins here is content worth publishing regardless, so the question isn't whether it's worth the risk. It's why you'd wait.
See ours in action: We practice what we're describing here. Fission's own machine-readable reference file lives at fissionagency.com/llms.txt, structured exactly the way this post recommends: plain text, answer-first, built for AI systems to read and cite.