SEO vs AEO vs LLMO is the wrong fight if you’re asking which one wins, but the real question for a SaaS content team with finite hours is which to invest in first, given your buyers, your sales cycle, your product, and your competition. This is a decision framework for that call, grounded in current data rather than the vendor panic pushing everyone toward AEO at once, and honest about where the industry consensus is quietly wrong.
Every week another tool launches promising to make you visible in ChatGPT, and another LinkedIn post declares SEO dead. The pitch is always the same: the ground has shifted, AI is eating search, and if you haven’t rebuilt your entire content operation around answer engines by Friday, you’re already invisible. It’s a good pitch, and it sells software, but it’s also the wrong frame for anyone deciding where a limited content budget should go this quarter.
The useful question was never which paradigm is best in the abstract. SEO vs AEO vs LLMO is really a sequencing problem: the three optimise for different surfaces, and the smart move is ordering them against your specific constraints rather than betting the budget on whichever acronym is loudest this month. For most B2B SaaS teams the answer looks boring: keep the SEO foundation compounding, layer AEO on top once that foundation is solid, and treat LLMO-first content as a targeted bet you make only when your buyers actually live inside language models.
The consensus is selling you a panic
The prevailing advice pushes every team toward AEO immediately, and the volume of it maps neatly onto how much money is chasing the category. There were dozens of answer-engine tools launched in a single stretch of 2026, each promising to do for AI search what SEO software did 20 years ago.
Lily Ray of Amsive, presenting at MozCon 2025, pointed out that around 95% of ChatGPT users still rely on Google, and AI search drives under 1% of total site traffic. For the overwhelming majority of SaaS companies, the traffic and the pipeline still come from search as it has worked for a decade, so a team that abandons a functioning SEO engine to chase 1% of traffic has confused where the puck is going with where the ball is right now. I’ve made the fuller version of this argument in why AEO won’t save a weak content strategy, and the short version is that AEO amplifies a foundation and can’t replace one.
AEO amplifies a foundation. Buy the amplifier before you’ve built the foundation and all you’ve bought is silence with better formatting.
SEO’s ROI is indeed diminishing, in specific places
SEO’s returns are eroding, but not evenly, and the unevenness is the whole strategic point. AI Overviews now sit on top of a large share of informational queries, and where they appear, they gut the click. Ahrefs found the presence of an AI Overview correlates with a 58% lower click-through rate for the top-ranking page, and Pew Research found people who saw an AI summary clicked a traditional result only 8% of the time, against 15% for those who didn’t.
The “SEO is dead” crowd skips where that erosion actually concentrates: informational, top-of-funnel queries, the definitions and how-tos that answer engines resolve on the page. Commercial and transactional queries, the ones closer to a purchase, still trigger far fewer AI Overviews, and Google has been pulling the feature back from those searches after testing it.
So SEO is splitting in two rather than dying off evenly; the awareness content that used to pull traffic is losing its click, while the bottom-funnel pages that actually convert are largely intact, which means if your SEO investment skews to the converting end, as it should, the erosion is slower than the headlines suggest. That’s also why building genuine SEO foundations still comes first for teams that don’t have them.
What AEO buys you, and where
AEO earns its place on a different metric than SEO, because the traffic it sends is smaller and better: Semrush found AI-search visitors convert around 4.4x better than traditional organic visitors, because someone arriving from a considered AI answer is further along than someone who clicked a blue link out of ten. AEO is a quality play rather than a volume one, and judging it by traffic numbers is the fastest way to conclude, wrongly, that it isn’t working.
The other half of the case is defensive: when an AI Overview appears, being cited inside it makes the difference between a trickle of clicks and none, since cited brands recover a meaningful share of the clicks that the overview would otherwise swallow. That makes AEO most valuable in verticals where AI Overviews have saturated the results, which right now skews heavily toward B2B technology, healthcare, education, and science. If your buyers’ informational queries reliably surface an AI Overview, AEO stops being optional, because the alternative is watching your best-ranking pages go quiet. The mechanics of doing it well live in my AEO/LLMO content optimisation framework, and the first step is an AI readiness audit to see where you stand before spending a euro optimising.
Where LLMO-first content pays off
LLMO is the frontier, which is where both the upside and the wasted money live, while AEO is about being extracted into an answer box on a search results page. LLMO goes a step further: being the source a model reaches for inside a ChatGPT or Perplexity conversation, often with no search and no click involved at all.
It pays off first where buyers research directly inside language models: developer tools, technical products whose evaluators ask Claude to compare options, and long, complex B2B sales where a single AI recommendation can shape the shortlist before a human ever visits your site. In a world where your next buyer might be a bot, being the cited source in that conversation is a real edge; the catch is that the tooling is immature and much of the popular tactical advice is noise. When I tested whether adding an llms.txt file does anything for AI visibility, the citation data said no, not yet. LLMO-first content is a bet worth placing only when you can name the specific buyer who makes decisions this way, and it belongs last in the funding order, well behind SEO and AEO.
The SEO vs AEO vs LLMO decision framework
Strip away the acronyms and the investment call runs on four variables. Score your situation against each, in order, because the order changes the answer.
- Audience. Are your buyers’ informational queries already dominated by AI Overviews, and do they research inside ChatGPT? Vertical is the fastest proxy: B2B tech, healthcare, and education sit at the high end of AI Overview saturation. If your buyers are there, AEO moves up the priority list, if they’re not, there’s no fire, and SEO keeps compounding quietly.
- Sales cycle. A long, multi-stakeholder B2B cycle means an AI recommendation early in the research phase can seat you on the shortlist or leave you off it, entirely without a click. Longer, more complex cycles push AEO and LLMO up the list, while short transactional ones keep the weight on the bottom-funnel SEO that still converts.
- Product complexity. A product that takes three meetings and a diagram to explain benefits disproportionately from being the source that AI cites when someone asks it to make sense of the category. If explaining your product is genuinely hard, being the clear, citable explainer is worth more than another keyword page.
- Competition. If competitors already own the traditional SERP, AEO can be an unoccupied lane where a smaller player leapfrogs into the answer. If the SERP for your terms is already AI-Overview-dominated, citation is close to the only game left, and defending it becomes urgent rather than optional.
Run those four through a simple sequence:
| Step | Question | If no | If yes |
|---|---|---|---|
| 1 | Do you have SEO foundations that rank and convert? | Build SEO first; AEO on nothing is wasted effort. | Continue. |
| 2 | Are your buyers’ queries AI-Overview-heavy or ChatGPT-researched? | Keep compounding SEO, add light AEO. | Fund AEO now to defend CTR and win citations. |
| 3 | Do buyers make direct, LLM-based shortlisting decisions? | AEO on top of SEO is enough. | Add LLMO-first content as a targeted bet. |
| 4 | Are you measuring citations rather than audit scores? | Fix measurement before scaling spend. | You’re allocating on evidence. |
Almost every team that thinks it has an AEO problem actually has a step-one problem wearing an AEO mask.
Run the four steps in order, because what’s right for a pre-revenue startup and what’s right for a mature platform in an AI-saturated category share almost nothing.
The mistakes that waste the investment
- The most common and expensive error is pouring money into AEO before the SEO foundation exists. Schema, FAQ blocks, and question-format headings bolted onto thin content produce a page that audits beautifully and gets cited by nobody, which is exactly the failure I broke down in why most teams run the AEO playbook backwards. The Princeton GEO study found the levers that actually earn citations are substance-side, such as attributed statistics and named sources, rather than formatting.
- The second mistake is measuring the wrong thing. A green AEO audit score tells you the formatting is tidy and nothing about whether a model actually pulls from you, so teams stay proud of a number that doesn’t move revenue. The only scoreboard that counts is whether AI systems cite you, which means tracking citations directly rather than trusting a checklist.
- The third mistake is the mirror image of the panic: abandoning the bottom-funnel SEO that still quietly converts because a conference talk said search was over. The pages nearest the purchase are the ones AI has touched least, and walking away from them to chase citations is how teams trade real pipeline for a vanity metric.
How my take differs from the consensus
The loud position in the market is that AEO is the new SEO and everyone should be all-in immediately. A quieter contrarian camp says it’s all hype and you should ignore it. I think both are lazy, because both skip the sequencing question that decides the whole thing.
My view is that traditional SEO still earns the most ROI for most B2B SaaS today, AEO becomes a high-impact amplifier the moment your foundation is solid and your vertical is AI-saturated, and LLMO-first content stays a sharp, narrow bet, reserved for teams whose buyers genuinely decide inside the models.
SEO vs AEO vs LLMO works as a sequence you run against your own situation, and the answer for a pre-revenue startup looks nothing like the answer for a mature platform in an AI-Overview-heavy category. A strategist’s job is to work out which situation you’re in, while vendors skip that and sell you the whole stack. That’s the difference between a content investment that compounds and one that just keeps pace with the discourse.
Frequently asked questions
For most B2B SaaS, SEO foundations come first because that’s still where the traffic and pipeline are, with around 95% of ChatGPT users also using Google and AI search under 1% of total traffic. AEO becomes the priority once your foundation is solid and your buyers’ queries are AI-Overview-heavy, which is common in B2B tech, healthcare, and education. LLMO-first content is the last and most targeted investment, worth it only when your buyers research and shortlist directly inside language models.
No, but its returns are splitting in two. AI Overviews sharply reduce clicks on informational, top-of-funnel queries, with the top result losing well over half its click-through rate where an overview appears. Commercial and transactional queries near the purchase still trigger far fewer AI Overviews and still convert, so the bottom-funnel SEO that drives pipeline is largely intact even as awareness content loses its click.
Check whether your buyers’ informational queries reliably surface an AI Overview, which depends heavily on your vertical, and whether AI-referred visitors convert well for you, since they tend to convert several times better than traditional organic traffic. If overviews dominate your category and you have a real SEO foundation to build on, AEO defends diminishing click-through and wins citations. If your foundation is weak, fix that first, because AEO on thin content gets cited by nobody.
AEO optimises to be the extracted answer on a search results page, such as a Google AI Overview or a featured snippet. LLMO aims one layer deeper: to be the source a language model reaches for inside a direct conversation in tools like ChatGPT, Claude, or Perplexity, often with no search and no click at all. AEO matters wherever AI Overviews sit on your buyers’ queries. LLMO is narrower, mattering only where buyers make decisions inside the models themselves.
