Over the last 30 days, we had the exact same discussion with all of our clients: Do we really need an AI-focused SEO plan now?
Yes, you do.
For years, Google shaped how we made choices online. Page 1 was the whole game. A list of links, all fighting for a click. Click or die.
That world search has changed, a lot.
LLMs like ChatGPT, Claude, Gemini, and Perplexity give you a direct answer in a couple of seconds. No scrolling. No comparison. Most brands don’t even get mentioned.
And suddenly the industry is full of new terms again: AI SEO, AEO, GEO, AIO…
The reality is simpler: SEO didn’t die, it just became harder.
Now you need to show up on Google and be referenced by AI tools. If your brand has low authority, no acronym will fix that. Strong foundations still matter more than anything else.
So the real question isn’t which term to follow, but how do you stay visible when AI is the one deciding what people see?
Welcome to the SEO in the era of AI. Clean, simple, and much less forgiving.
Search used to be simple. Google showed a list of links, and we chose which one deserved our attention. You’d check a headline, skim a snippet, and decide which result felt right.
We trusted ourselves to pick the answer.
That’s not how it works anymore. AI doesn’t give you options. It decides for you. Google AI Overview and LLMs like ChatGPT, Claude, Gemini, and Perplexity give a single answer in seconds, and most brands never even appear in that output.
So what changes for SEO? A lot.
Visibility is no longer about getting the top spot on a page of ten links, but now means your content gets pulled into AI’s answer. If it doesn’t, you’re simply not part of the conversation.
The fight isn’t for a click anymore, but for AI to recognize you at all.
And that’s the real shift, SEO isn’t only about ranking first but proving you’re a source worth citing.
A year ago, most B2B buyers didn’t take AI seriously. The majority said it had zero influence on their decisions, and only a small group found AI tools useful at all. Trust was the blocker.
That’s not the case anymore. Today, most buyers say they see Google’s AI Overviews in their searches, which tells you everything you need to know.
And it works because it’s easy. You ask something, AI gives you the answer. No back-and-forth. No digging through links. No comparing sources.
What’s even more surprising is how quickly trust moved with it.
Almost everyone now says they trust AI-generated answers as much as human-written content. In other words, we stopped being the ones who choose the “best” answer, we assume AI already did that work for us.
The trust shifted from the user to the system.
Yes, ranking on Google still matters a lot, but it’s no longer the whole picture. What really matters now is whether AI recognizes you, understands you, and includes you.
Authority judgment has shifted from the algorithm and the user to the model alone.
And if the model doesn’t see you as a trusted source, you’re out.
The pressure in B2B tech isn’t new. Grow fast, take every chance you get, and do it with limited time, limited money and limited people. That part hasn’t changed.
What changed is what counts as visibility. Traffic and keyword rankings don’t tell you anything anymore.
AI search doesn’t care who ranked first only, but also cares who it trusts.
If your brand isn’t mentioned in AI answers, you’re not in front of buyers, and most buyers rely on AI more than they admit ;).
That’s why AI SEO matters, not as a trend, but as the new layer on the top of your existing SEO efforts.
Tech brands that get this shift will manage to own their category, and cut the CAC, but the ones that don’t will end up where forgotten links always go, the modern version of page 9 on Google, or in simple words, invisible.
Example of AI Overview in Google Search
When you search on Google now, the first thing you see isn’t the links, it’s the AI Overview. AI overview pulls everything it considers relevant into one block and sets the direction for the entire search. Google still owns the market, but AI Overviews already shapes most queries.
AI tools use the same sources.
If you appear in these overviews, you’re in the data AI pulls from.
If you don’t, you’re out. Simple.
For B2B tech brands, getting mentioned in Google's AI Overview is critical.
A lot of teams still track SEO like it’s 2015. Most of those metrics don’t mean anything when it comes to AI-driven search. Some may even distract you from what actually matters.
Here’s what you need to stop obsessing over:None of these show whether AI considers your content useful. Let's have a closer look why:
If the traffic doesn’t match with what you offer, it’s noise. It won’t lead to revenue and it certainly won’t help AI understand what you’re about.
Ranking for a keyword means nothing if AI ignores the content behind it. If the model doesn’t see value and depth in your content, well... the ranking carries no weight.
These metrics explain user actions, not AI decisions. Whether someone stayed for 2 seconds or 2 minutes doesn't influence if AI cites your page.
Large numbers don’t mean you’re relevant. AI won’t pull from your content just because many people viewed it. AI pulls from content it considers reliable!
AI rewrote how visibility works, so the metrics that matter changed with it.
Instead of tracking irrelevant KPIs in 2026, you will need metrics that show whether AI sees your content as credible, consistent, and worth pulling into answers.
These are the ones worth paying attention to:
These metrics reflect whether AI can rely on your brand when generating answers. Let's have a closer look with some examples:
If you’re a B2B tech brand that sells security automation, AI expects you to cover that topic widely and consistently. Not one article, not a landing page, but a comprehensive pillar page.
Example:
If someone asks, “How do mid-sized companies automate incident response?” AI won’t pull from a site with a 500-word blog post (it might, if you're TechCrunch!), but it will look for a source that elaborates the topic in-depth. It will pull from a brand that has:
Topical authority tells AI: “This brand knows the subject well enough to be cited.” Without it, you’re ignored.
Depth beats volume, period. AI doesn’t want thin pages with generic paragraphs. It wants useful answers.
Example:
Two companies write about “AI governance.”
AI will pick company B because the content strategy reduces uncertainty. Depth is a credibility signal, and artificial content is a quick path to invisibility.
AI checks if your message stays consistent consistent across your site, your LinkedIn, press mentions, Reddit, GitHub, external blogs, etc.
Inconsistency weakens trust.
Example:
If your website says you’re an “ML monitoring platform,” but your LinkedIn page says you’re an “AI governance tool,” and your founder describes the product as “data quality automation,” AI sees fragmentation. Fragmentation equals low trust.
Consistency tells AI your brand is stable and reliable.
You won’t see this in Google Analytics, but you’ll sure notice it in how AI paraphrases your work.
Signs you’re getting cited by AI models:Example:
If ChatGPT starts answering questions about “synthetic data for pharma” in a way that mirrors your explanations, you’re already influencing the model. This is the new organic visibility, even if the model doesn’t show your brand name yet.
AI cares whether your content matches the question behind the query, not the keyword itself.
Example:
Query: “Best ML compliance tools for pharma.”
A generic page about “AI governance” won’t matter.
A page that covers pharma-specific compliance constraints, with examples and workflows, will get chosen.
If the content doesn’t match the actual intent, AI filters it out immediately.
The metrics that matter now have nothing to do with chasing numbers.
They’re about proving to AI that your brand understands its topic, speaks clearly, and stays consistent.
Optimizing your brand for LLMs it’s about making your website structured, consistent, and credible enough that AI systems trust it when deciding what to surface.
LLMs rely on signals: topic depth, clarity, structure, source consistency, real authority.
If these aren’t present, you won’t show up. Not on Google’s AI Overviews, not in ChatGPT or Perplexity outputs.
Here’s what you should be doing:
A good pillar page isn’t long for the sake of being long. It’s long because the subject requires depth. When a company builds a proper hub on synthetic data evaluation or collaboration workflows or incident response automation, AI sees that as a signal: “this brand understands this problem space.” It’s one of the few moments where structure directly influences credibility. This is exactly why the pillar pages we’ve built for brands like Modulos, Syntheticus or WEDO work, because they aren’t content mills, they’re organized knowledge.
A good pillar page should:
If your site has scattered articles with no hierarchy, AI sees noise. If your site has clean subject clusters, AI sees clarity, and clarity becomes trust.
Example (B2B tech context):
Synthetic data pillar page:
- What it is
- When to use it
- How it behaves in training pipelines
- Regulatory considerations
- Limitations and failure cases
- Links to deep dives (privacy risk, bias transfer, evaluation)
A hub like this tells AI: “This brand doesn’t just publish content. It understands the subject.”
AI systems don’t “understand” your pages the way humans do.
They rely on structured definitions (schema) to map meaning. Without schema, LLMs have to rely entirely on text patterns to understand what your page contains. With schema, it gets a clear map. If you don’t give structure, AI fills in the blanks. And AI is terrible at guessing.
Schema matters because it:
For example, if you’re a governance platform, and your schema clearly ties pages to:
“model monitoring”
“bias evaluation”
“audit trails”
“compliance workflows”
AI learns the context of your product much faster than reading paragraphs.
Here's our favorite part. Most B2B tech companies still write content from their own perspective:
• What the product does
• What the features are
• What the value is
AI doesn’t care. It doesn't give a sh*t about what you internally think about your platform.
Where most companies fall apart is in content relevance. Not “relevance” in the SEO sense, relevance in the actual ICP sense. AI is getting very good at identifying what specific buyer groups consistently complain about, ask about, and search for. If your content is still written around your internal messaging instead of real customer pain, AI will ignore it. A buyer searching for “how to validate model behavior for FDA audits” isn’t looking for a paragraph about your “all-in-one governance platform.”
When your content mirrors real ICP problems, AI sees relevance and matches your pages to more queries.
AI doesn't reward you for publishing nonstop. It rewards clarity and consistency.
Repurposing content also matters more than people think. Not because you need more assets, but because repetition creates stronger patterns for AI models. When an idea shows up repeatedly across formats (guide, webinar, Q&A section, LinkedIn post, internal glossary), the model treats it as intentional. It becomes part of your brand’s semantic gravity. And that’s what helps you show up when a user asks a question even loosely related to your domain.
If one piece captures a topic well, repurpose it instead of diluting your expertise with new half-baked content.
LLMs are pattern-matching systems. When they see the same concept explained consistently across formats (page, FAQ, video, LinkedIn...), they treat it as a stronger signal.
For example, you explain “model drift in regulated industries” clearly in one guide.
Repurpose it into:
Same idea. Different angles. This is how you train AI to associate your brand with a topic.
The biggest shift in AI SEO is authority, the kind that comes from visibility across multiple credible sources. Every tech founder now asks some version of the same question:
“How do we get ChatGPT to mention us?”
Our answer to this isn’t fun, but it’s honest: you need a footprint that AI can actually detect. A footprint means your message shows up consistently across your site, third-party platforms, communities, technical discussions, and reference points that AI models are trained on.
If your site has weak internal linking, shallow pages, unclear product positioning, or inconsistent messaging, AI will not trust you. Solid technical SEO + structured hubs = baseline.
AI relies on external references more than people think.
These help:
AI cross-checks all of this to decide whether your brand is a serious entity or just noise.
Strucutre your content in a Q&A format, and give your brand the chance to be referenced by AI when users look for answers. LLMs pick up pages that are structured for clarity. Don’t bury an answer 12 paragraphs down. Put it in the first 2 lines.
AI rewards precision:
You’re not writing to rank anymore. You’re writing to be used!
If you want your brand to show in AI-driven search, your strategy has to go deeper than “optimize this page.” Buyers are relying on models to form opinions long before they land on your website.
If you want to be in those answers, and turn that into revenue, reach out.
We can help you build something that AI recognizes, because we already did it for quite some companies.