There was a time when “just Google it” was the answer to everything. That’s why marketing teams obsessed over climbing to the top of the search engine rankings. It was the only way to constantly grab customer attention, prove authority, and earn trust.
Now, a massive portion of your customers are probably skipping Google altogether. They’re asking ChatGPT about which products to buy, Gemini for advice on comparing vendors, and their smart assistant to order what they need for them. Retailers are already losing a serious chunk of their shoppers just because they’re not thinking about generative engine optimization as much as SEO.
This isn’t a passing trend. Google’s AI Overviews now show up in billions of searches a month. Retail analysts said Black Friday 2024 was the first year where brands were judged on “AI discoverability,” not traditional SEO. Meanwhile, ChatGPT is quietly reporting “hundreds of millions” of people using it for shopping help.
If you’re a CMO or part of a marketing team right now, and you actually want to exist to customers next year, it’s time for a strategy change.
Understanding AI-Readable Content
Most marketing leaders trying to reach their performance goals are still living with the comfortable belief that they’re “doing well” just because their content ranks on Google. High rankings are great, but in the long term, they’re not going to mean much if AI systems can’t process what you publish.
The sad truth is that most teams haven’t adapted to that yet. When people talk about AI-readable content, or even “generative engine optimization”, they often picture fancy formatting tricks. It’s not that. LLMs don’t care about your brand voice, they don’t admire your clever metaphors, and you can’t sweet-talk them with meta descriptions.
Really, what they need is structure, precision, and clarity. Imagine you’re writing for a very fast, very logic-driven colleague, like Data from Star Trek.
The good news is that giving machines that isn’t that tough. Most of us have been using structured headers, short paragraphs, and clear bullet points to make things more “skimmable” for humans for years. With machines, you just have to be even more direct, and a lot more consistent.
If your product descriptions say one thing on your website and a slightly different thing on LinkedIn, or in a press release, or in your support docs, you’ve basically created a semantic funhouse. Models get confused, and then they don’t cite you.
Now, this seems like all you have to do to earn AI visibility is make your content as boring and straightforward as possible. Obviously, there’s more to it than that. You still need to show authority; you still need to make the machines (and the people behind them) trust you. Plus, you’re still going to need to have some human spirit if you’re ever going to connect with the other people in the world reading what you publish. Clarity is just a starting point.
Getting Clear on Generative Engine Optimization: SEO to GEO
Some days I think the marketing world secretly enjoys reinventing its vocabulary just to keep everyone on their toes. These days though, you are going to need a basic understanding of a few new terms if you’re going to keep up with all this. So here are the main ones.
Let’s start with AEO, because it’s honestly the most brutally direct of the three. Answer Engine Optimization is exactly what it sounds like: you’re not trying to land on page one anymore, you’re trying to win the answer. Google’s AI Overviews, Gemini’s instant cards, and Perplexity’s citations all look for short, precise, almost boringly clear statements. A definition, a step-by-step, or a clear comparison.
Generative engine optimization is where things get fun. GEO is less “SEO tactics” and more “teach the machines who you are.” Models build their understanding of a brand the same way someone builds a memory: from repetition, context, and trustworthy sources. They absolutely favor semantic footprint over keyword footprint. I’ve seen brands chase keywords while their competitors quietly build credibility on Reddit threads, industry roundups, analyst quotes, you name it, and guess which ones AI recommends first?
LLM SEO is the pragmatic sibling in all this. It’s about giving AI something it can quote without hallucinating. Long-form queries (20 words and up), verifiable claims, modular “bite-sized” sections. Our coverage of AI-driven personalization makes this even clearer: predictive systems only work if the underlying content for AI is consistent and structured.
Generative Engine Optimization and the Rise of Machine Customers
One thing that really makes all of this “AI optimization” more important right now is that you’re not just trying to capture the attention of AI search engines anymore. Yes, you want to appear in Google overviews, and get recommended by ChatGPT, but you also need content that’s going to appeal to the new generation of customers emerging now: bots.
We’ve talked about machine customers quite a lot lately, because increasingly, they’re the underserved segment that teams need to be thinking about. Often, it’s not a “human” doing the early research on your product anymore, it’s an AI agent. You’ve got bots crawling through your pages, your docs, and your pricing tables, trying to piece together whether you’re worth recommending. You need to convince them you are.
Just like ChatGPT or Microsoft Copilot, these things don’t read like humans. They check your pricing pages the way engineers check an API response: “Is the data clear? Is anything contradictory? Does the naming make sense?” Humans can tolerate a little chaos. Machines can’t. One ambiguous sentence, and they just move on to the next vendor.
Most companies really aren’t prepared for this future. We recently talked about a study where only 3 out of 42 companies were actually capable of serving machine customers across email and chat. If you can’t help a bot out with a service request, chances are you can’t fine-tune your marketing content to appeal to machine customers either.
Generative AI Optimization: How to Design AI-Readable Content
Alright, so we’ve covered all the complicated stuff about what AI-readable content and generative engine optimization are, and why they’re important. Now it’s time to get down to some strategy, because most businesses are still painfully lost.
Here’s the basic playbook that’s actually starting to work.
Use Answer-Framing: The Non-Negotiable GEO Technique
This is probably the simplest first step. If you’re trying to connect with customers asking questions (even machine customers), start your content by giving them the answer. Every page you create needs a simple opening paragraph that addresses the specific query you’re targeting.
If you’re creating a product page, immediately answer these questions:
- What is it?
- Why does it matter?
- How does it work?
- Who is it for?
This matches how LLMs skim; they grab the “first nugget” and decide if you’re worth citing. If you bury the definition, Gemini shrugs and recommends someone else.
Structure for Extractability
Models “read” even less than people do. But they’re not looking for a story, they’re looking to “extract” information. So give them surfaces they can latch onto:
- Question-based H2s (“What is X?” “How does X work?”).
- Short paragraphs, under 120 words, ideally shorter.
- Comparison tables that the model can lift directly.
- Numbered steps, lists, TL;DR sections, “Fast Facts” boxes.
- Bold, explicit section labels.
Studies are starting to show that predictable structure increases both retrieval and the variety of ways models paraphrase your content. Humans like clarity. Machines depend on it.
Add Schema & Metadata Everywhere
Okay, so there is a bit of a technical element to generative engine optimization, but not a huge one. Schema is just the code you’ve been adding to your pages anyway to help search engines understand what you’re talking about. All you need to do is clean it up for LLMs.
Google’s AI Overviews weigh structured data heavily, and Gemini is even stricter. Use:
- Clear canonical URLs
- Clean HTML
- Alt text that describes meaning (“workflow logic,” not “blue circle pattern”)
Leave any clever wording out of this part; it won’t help.
Build Robust FAQ Libraries
LLMs love FAQs. They recombine them constantly. A single FAQ section can power 30 different mid-funnel prompts. The best way to make sure that your FAQs are valuable for both bots and human customers, is to make sure they’re actually relevant.
Your search logs, sales calls, and support tickets should give you a pretty good idea of what people are asking; use that to your advantage.
Provide First-Party & Zero-Party Data
Models trust what they can verify. Use proprietary benchmarks, surveys, performance data, or even simple pilot results. Semrush’s 2025 analysis showed pages with real evidence saw a 30–40% increase in AI citations. That’s enormous.
Adding extra credibility cues helps, too. Just like you use EEAT for SEO, use it for generative engine optimization too. Create pages with:
- Clear author bios
- Credentials
- Methodology notes (“data from 412 customer interviews…”)
- SME quotes instead of generic statements
Remember, expertise and trust aren’t marketing assets anymore; they’re infrastructure for GEO.
Strengthen Entity Consistency Across the Web
This part should be something you’re doing already, but just in case it’s not, check consistency in your content. If your product name, description, pricing, positioning, or even your founder bio changes across platforms, LLMs treat you like an unreliable narrator.
Use consistent:
- Product names
- Titles
- Claims
- Messaging
- Definitions
Every so often, double-check that nothing published about your company contradicts itself.
Measure Performance the Right Way
One final thing. If you’re trying to appeal to AI search engines and machine customers as well as human beings, remember that your metrics are going to change. You’re not just focusing on website clicks anymore, you’re looking for:
- AI Overview inclusion (Google deciding you’re worth showing in its summarized answers).
- Citation frequency in ChatGPT, Gemini, Perplexity, Claude.
- Share of answer: What percentage of a generated response comes from your pages.
- Prompt clusters Where your brand appears (“best CRM for SMBs,” “top CCaaS platforms,” etc.).
- Sentiment of AI-generated summaries (good or bad)
- External mention growth across Reddit, LinkedIn, Quora, forums.
- AI-referred traffic (growing fast even when Google traffic stalls).
- Conversions that start with an AI interaction.
Always remember, if AI puts you on the shortlist, that’s money. If it doesn’t, no human ever sees you. At least, not most of the time.
Prepare for Generative Engine Optimization Now
There’s a weird denial running through marketing teams right now. Everyone knows search is changing; you can feel it in your gut every time you type a question into ChatGPT instead of Google. But companies still aren’t making any real changes.
Honestly, that could be a good thing. The moat is wide open. Most companies don’t have their schema sorted. They don’t have entity consistency. Their FAQs are a graveyard, their internal data contradicts their public content, and their governance is loose enough to cause the kind of AI-agent leak we write about on CX Today.
All of that is fixable. And the brands that fix it now will own the recommendation layer: the part of the funnel where assistants quietly decide which companies get handed to humans.
If you’re ready to get ahead, start with our ultimate guide to sales and marketing technology, and make sure you have the tools you need in place to prepare for the new age of AI search and machine customers.
