The real shift: you’re no longer just marketing to humans
Look at those headlines again and strip out the hype. Underneath all the “ChatGPT SEO tools,” “AI misinformation experiments,” “custom GPT workflows,” and “AI agents are coming for you” is one blunt reality:
You’re not just marketing to people anymore. You’re marketing to the machines that advise those people.
Search engines, social feeds, and inboxes are no longer the only filters. LLMs, agents, and AI “shopping assistants” are becoming the new discovery layer. They decide:
- Which brands get mentioned in answers.
- Which products get recommended first.
- Which sources are treated as “authoritative” or ignored.
For performance marketers and media buyers, this isn’t a thought experiment. It’s a targeting, measurement, and creative problem that will compound over the next 12-24 months.
From SEO to AEO to LLMO: the acronym soup hides one question
AEO, GEO, LLMO, “ChatGPT SEO,” “AI agents” – all of this is really one question:
How do I become the default recommendation when an AI is answering my customer’s question?
Whether it’s:
- “What’s the best email tool for a 5-person SaaS team?”
- “What’s a good running shoe for flat feet under $150?”
- “Write me a 3-email sequence for abandoned carts for a skincare brand.”
The AI is picking winners. You want to be one of them.
That’s not classic SEO. It’s not just “ranking.” It’s being:
- Credible enough to be cited.
- Structured enough to be parsed.
- Specific enough to be chosen for a niche scenario.
Why this matters to performance marketers right now
You might think: “Cool, but I buy clicks, impressions, and conversions. I don’t optimize for ChatGPT.”
You actually do. You just don’t call it that yet.
AI is already sitting between your spend and your results:
- Media platforms use LLMs and models to rewrite your ads, expand keywords, and auto-generate creative.
- Users are asking AI tools what to buy, how to compare, and which brands to trust before they ever hit your site.
- Content surfaces (search results, social feeds, inboxes) are increasingly summarized, clustered, and filtered by AI.
That means your job is shifting from:
- “How do I get in front of the user?” to
- “How do I get in front of the AI that’s in front of the user?”
Three realities operators need to accept
1. AI agents will compress messy middle research
The “messy middle” of search – comparing, reading reviews, bouncing between tabs – is being compressed into prompts:
“Compare Klaviyo and Omnisend for a Shopify store doing 50k/month. I care most about deliverability and templates.”
If you’re not present in the sources that AI trusts for that query, you’re gone before the prospect ever hits a browser.
2. AI is already a media channel, even if you can’t buy it yet
You can’t “buy” ChatGPT placement (yet), but users are spending attention there. That makes it a channel whether you like it or not.
Treat it like early SEO:
- You can’t control it directly.
- You can influence it with smart, structured, useful content.
- Those who start now will be the default answers later.
3. Your brand is being summarized by systems you don’t control
Tools are generating:
- “Best X for Y” lists.
- Product comparisons.
- How-to flows that mention tools and vendors.
If you don’t actively shape the raw material those systems consume, you’re letting random affiliates, outdated reviews, and scraped copy define you.
Practical playbook: marketing to AI agents
Here’s how to treat AI agents as an actual channel, not a thought-leadership talking point.
1. Design content for “agent use cases,” not just keywords
LLMs answer tasks, not just queries. Start from the tasks your buyer asks AI for help with.
Examples:
- “Create a 90-day content plan for a DTC skincare brand.”
- “Build a budget split across Meta, TikTok, and Google for a $50k/month ad spend.”
- “Give me a 7-day workout plan for a beginner runner.”
For each task, ask:
- What artifacts would an AI want? (checklists, templates, frameworks, parameterized examples)
- How can my product or brand show up naturally in that artifact?
Then create assets like:
- Step-by-step playbooks with clear headings and numbered steps.
- Tables comparing tools, tiers, or scenarios.
- Concrete examples with real numbers, inputs, and outputs.
LLMs love structure and specificity. Give them both.
2. Make your site “LLM-readable,” not just crawlable
Classic SEO: “Can Google crawl this?”
AI-era SEO: “Can an LLM understand, quote, and reuse this?”
Tactical moves:
- Use explicit, literal language. Don’t bury what you do in fluffy positioning. Say “Email marketing software for Shopify stores” if that’s what you are.
- Add concise, factual summaries. Short intros that clearly state who it’s for, what it does, and when to use it.
- Use schema where it actually helps. Product, FAQ, HowTo, and Review schema give machines more structured context.
- Standardize naming. Call your features the same thing everywhere. LLMs struggle with brands that rename the same concept five ways.
3. Seed the “source graph” AI pulls from
LLMs over-index on:
- High-authority domains.
- Frequently-cited resources.
- Well-structured lists and guides.
Your job is to show up in those inputs.
Practical ways:
- Be in real comparison content. Work with credible reviewers and partners to create honest “X vs Y” content where you’re fairly represented.
- Publish your own comparison pages. Transparent, data-backed, and non-sleazy. LLMs will still use them as reference.
- Contribute to “how-to” ecosystems. Guest content, co-marketing, and case studies on sites that already rank and get scraped.
You’re not gaming the model. You’re feeding it better raw material.
4. Treat AI prompts as a new kind of keyword research
Traditional keyword research tells you what people type. Prompt research tells you what they ask AI to do.
Build a simple prompt research loop:
- Ask AI tools how your ICP would search for solutions like yours.
- Collect the exact prompts and questions they generate.
- Map those to content, landing pages, and offers.
Then test:
- Use those prompts as seed ideas for ad copy, hooks, and landing page angles.
- Watch which angles drive better CTR and conversion.
- Feed the winners back into your content and FAQ strategy.
5. Build “agent-friendly” landing experiences
A dark landing page beat “best practices” in an A/B test because it matched context and intent, not a checklist. Same idea here: build pages that work for both humans and machines.
For each key page, ask:
- Can an AI summarize this page in 2 sentences and be mostly right?
- Is it obvious who this is for and when they should use it?
- Are there clear, scannable sections that map to sub-questions?
Concrete tweaks:
- Use intent-based subheads. “Pricing for agencies,” “For stores under $50k/month,” “Migration in under 7 days.”
- Add short, direct FAQs phrased as the questions people (and AI) actually ask.
- Include worked examples with numbers, screenshots, and outcomes. LLMs love citing these.
6. Instrument for the AI era: new attribution clues
You won’t get “ChatGPT” as a referrer in GA. But you can spot AI-influenced demand.
Watch for:
- Weirdly specific branded search. Queries like “yourtool vs competitor for 10 person b2b team” appearing out of nowhere.
- Sales calls referencing “I asked ChatGPT…” Train reps to log this. It’s a leading indicator.
- Sudden spikes from long-tail content that reads like prompt fodder (detailed how-tos, frameworks, templates).
Simple moves:
- Add “How did you first research this problem?” to lead forms with “AI assistant” as an option.
- Have sales and success teams tag “AI-influenced” deals in your CRM.
- Track which content pieces are most often cited or copy-pasted by prospects.
How this changes media buying decisions
This isn’t just a content or SEO problem. It changes how you think about budget and channels.
1. Buy attention that creates durable AI surface area
Not all paid media has the same “half-life” in an AI world.
High half-life:
- Sponsoring or co-creating in-depth guides and benchmarks.
- Paid placements that live on evergreen pages (tools lists, resource hubs).
- Partnerships that result in long-lived content on high-authority domains.
Low half-life:
- Ephemeral social ads with no landing content worth scraping.
- Short-term campaigns that never produce a reusable asset.
Shift a slice of budget into campaigns that produce indexable, quotable assets, not just in-feed impressions.
2. Align creative with how AI rewrites and summarizes
Platforms are already rewriting your headlines and descriptions. Assume your copy will be:
- Shortened.
- Paraphrased.
- Combined with other messages.
So:
- Make the core claim brutally clear in one sentence.
- Use numbers, constraints, and qualifiers (“for X,” “under Y,” “in Z days”).
- Avoid cleverness that collapses when summarized.
3. Test offers that match AI-shaped expectations
If AI tells users “Look for tools with X, Y, Z features and a free trial,” guess what they’ll expect when they hit your page.
Pay attention to:
- What AI tools say “matters” in your category (features, guarantees, pricing models).
- How they describe “good” vs “bad” options.
Then test:
- Guarantees or policies that match those expectations.
- Bundles or plans named in plain language that mirrors AI descriptions.
- Landing page sections that explicitly address the criteria AI is teaching buyers to care about.
What to do in the next 90 days
To make this concrete for operators with a P&L and a calendar:
-
Audit how AI currently describes you.
Ask multiple AI tools:- What is [your brand]?
- Who is [your brand] best for?
- What are alternatives to [your brand]?
Note what’s missing, wrong, or unhelpful.
-
Map 10-20 “agent tasks” for your ICP.
These are the prompts they’d realistically use. Prioritize tasks tied to high-intent buying moments. -
Create or upgrade 3-5 “agent-grade” assets.
Detailed, structured, example-rich pages that directly answer those tasks and are easy for both humans and LLMs to reuse. - Shift 5-10% of paid budget into campaigns that produce durable, indexable content on your domain or partners’ domains.
- Add AI influence tracking to your forms, CRM, and sales process. Start measuring the impact, even if the data is messy at first.
The operators who treat AI agents as a real distribution layer – not a novelty – will quietly compound an advantage while everyone else is still arguing about acronyms.