The real shift: you’re not just marketing to humans anymore
Across those headlines, there’s one pattern that actually matters to performance marketers and media buyers:
You’re no longer just marketing to people. You’re marketing to AI intermediaries that decide what people see, read, and buy.
Search results are increasingly AI answers. Social feeds are LLM-curated. “ChatGPT Shopping” is literally a buying journey where the assistant, not the browser, is the interface. AI agents are starting to pick products, summarize content, and filter options for users.
That means your job is quietly shifting from “rank higher” and “win auctions” to something more brutal and more interesting:
Make your brand and offers the obvious, low-risk choice for AI systems whose only goal is: don’t be wrong.
From SEO to AEO to LLMO: the same game, different referee
Everyone’s throwing around acronyms: AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), LLMO (Large Language Model Optimization). Ignore the naming war. The mechanics are similar:
- There’s a system sitting between you and the user.
- It tries to predict the “best” answer, product, or path.
- It optimizes for low risk, high relevance, and user satisfaction.
In search, that system is Google. In social, it’s the feed ranking algorithm. In AI-first journeys, it’s the LLM or agent stack.
What’s changed is the surface area and the stakes:
- AI answers compress choice. One or two options, not ten blue links.
- AI agents will increasingly transact on behalf of users, not just recommend.
- Misinformation and hallucinations are real, so models are biased toward “safe, consensus, well-structured” sources.
If your media and content aren’t built to be “safe, obvious picks” for these systems, your performance is going to quietly decay while your dashboards still look “fine.”
What AI systems actually want from you
Forget the hype. LLMs and AI agents are pattern matchers with a strong fear of being confidently wrong. They reward:
- Clarity – clean structure, explicit claims, and clear entities.
- Consensus – alignment with what other credible sources say.
- Evidence – data, examples, and references they can paraphrase.
- Stability – URLs and page structures that don’t constantly change.
- Coherence – content that’s internally consistent and not spammy.
Now look at your current performance setup:
- Landing pages built for “clever” A/B tests, not clarity.
- Blog content written for keyword density, not structured answers.
- Offer pages that hide pricing, eligibility, or specs behind clicks.
- Ad copy that overpromises and sends people to generic pages.
That might still work for humans who are motivated enough to dig. It’s terrible for AI systems that need to scan, summarize, and commit to a recommendation in milliseconds.
AI misinformation experiments: the risk you’re not budgeting for
Those AI misinformation experiments you’re seeing in headlines aren’t just academic. They’re a preview of your next performance problem:
- LLMs hallucinate product specs, pricing, and availability.
- They mix your brand with competitors in weird ways.
- They confidently present outdated or wrong info as fact.
For a performance marketer, that’s not a PR issue. It’s a conversion leak and a CPX tax:
- Prospects show up with wrong expectations.
- Your CS and sales teams spend time correcting AI’s mistakes.
- Refunds, churn, and low intent traffic creep up while you “optimize creatives.”
You can’t fully control LLM outputs, but you can reduce the model’s temptation to make things up about you.
Designing your brand to be “AI-friendly” (without drinking the Kool-Aid)
Here’s a practical way to think about it: if an AI assistant had to “sell” your brand or product in 3-5 bullet points, how easy are you making that job?
Let’s break this into things you can actually do in the next 3-6 months.
1. Build canonical answers for your highest-value questions
Start where money changes hands. For each core product or offer, define:
- What is it?
- Who is it for?
- What problem does it solve?
- What are the key features / specs?
- What are the constraints? (pricing, eligibility, geography, terms)
Then:
- Create a single, canonical page that answers each of those, clearly and explicitly.
- Use simple headings, bullets, tables, and FAQs. Think “LLM-friendly schema” even if you’re not using formal schema markup yet.
- Keep that page stable. Update content in place instead of spinning up endless variants.
Goal: if a model crawls your site, there’s one obvious, low-ambiguity source of truth it can compress into an answer.
2. Fix cannibalization and content clutter
Those “cannibalization” and “8,000 title tag rewrites” case studies are pointing at the same issue: too many weak, overlapping pages confuse both search engines and LLMs.
For AI-driven journeys, clutter is worse than invisibility. If a model sees 20 similar pages from you with conflicting claims, it will:
- Average them into something bland, or
- Skip you in favor of a competitor with a cleaner signal.
Action plan:
- Audit your top 100-500 URLs by traffic and revenue contribution.
- Cluster by intent (e.g., “pricing,” “how it works,” “comparison,” “implementation”).
- Merge or redirect overlapping pages into a single, stronger asset per intent.
- Standardize title tags and H1s to match the actual intent, not just keywords.
This is boring work. It’s also the kind of work that makes you look like a “trusted, consistent” source to AI systems.
3. Treat your landing pages as training data, not just test fodder
That dark landing page that beat “best practices”? Great. But your job now isn’t only to win the A/B test. It’s to make the winning variant legible to machines.
For each high-spend funnel, ask:
- Can an LLM instantly identify the offer, audience, and main benefit?
- Are the key claims stated in text, or buried in images/video only?
- Is there a clean, text-based summary of the offer near the top?
- Are you using consistent language across ads and landing pages?
Practical tweaks:
- Add a short, structured “At a glance” section near the top of the page.
- Turn your hero copy into something that could be pasted into an AI answer without editing.
- Use the same phrasing for your core benefit across channels so models see a strong pattern.
4. Stop feeding AI garbage about your own brand
Self-promotional “best” lists and thin AI-written content might give you a short-term bump in some corners of ChatGPT, but they’re also:
- Training models that your brand publishes fluff.
- Creating conflicting claims about your own positioning.
- Raising the chance that models treat your content as generic filler.
Instead of spamming “Top 10 X” posts, create a small set of deep, factual, reference-grade pieces that an AI system would actually want to quote:
- Original data or benchmarks.
- Clear how-tos with steps and edge cases.
- Transparent comparisons (including where you’re not the best fit).
Think “what would I want a model to say about us in 2-3 sentences?” Then write the piece that makes that summary inevitable.
5. Reframe “learning periods” as training signals, not just platform quirks
Media buyers love to complain about learning periods in Meta, Google, TikTok. Underneath the annoyance is a useful mental model for AI-first marketing:
Every change you make is a new hypothesis the system has to test about who you are and who you’re for.
That applies beyond ad platforms. Constantly changing your messaging, pricing, or structure without a plan means:
- Search engines keep re-evaluating your relevance.
- LLMs keep seeing conflicting patterns about your brand.
- Recommendation systems struggle to place you confidently.
Operationally, this means:
- Batch your changes. Don’t drip-feed micro-optimizations that reset learning.
- Document “canonical” messaging and stick with it across channels.
- Treat any big repositioning as a 3-6 month re-training window, not a 2-week test.
Media buying in an AI-first funnel
So what does this mean for your day-to-day as a performance marketer or media buyer?
1. Plan for AI-influenced paths, not just last-click journeys
Users will increasingly:
- Ask an assistant what to buy.
- Get 1-3 options.
- Then go to social, search, or marketplaces to sanity-check.
Your media strategy should assume:
- Some users discover you via AI answers you can’t directly attribute.
- Others discover competitors via AI and only see you as a comparison point.
- Assistants will bias toward brands they “know” and can describe cleanly.
Implication: you need both brand presence in AI answers and strong retargeting / mid-funnel capture for those sanity-check moments.
2. Treat AI surfaces as emerging “inventory”
You can’t bid directly on “top slot in ChatGPT Shopping” yet, but you can:
- Ensure your product feeds, specs, and reviews are clean and consistent.
- Align naming conventions across site, feeds, and marketplaces.
- Use structured data where possible (schema, product markup, FAQs).
Think of this as pre-buying future inventory: you’re making sure that when AI-driven surfaces become more commercialized, your brand is already easy to plug in.
3. Rebuild your reporting to notice AI-driven shifts
AI influence will often show up as “weird” patterns in your existing data:
- Branded search spikes without matching campaign changes.
- New, long-tail queries that sound like paraphrased AI prompts.
- Higher intent from certain geos or devices where assistants are more used.
Practical moves:
- Segment branded vs non-branded performance more aggressively.
- Mine search term reports and site search logs for AI-style queries.
- Ask new customers directly: “Did you use an AI assistant while researching?” and tag the answers.
The operators who win this shift
The winners in this AI-agent world won’t be the ones who read the most think pieces. They’ll be the ones who quietly:
- Cleaned up their content and landing pages.
- Standardized their messaging and offers.
- Stopped training models with junk.
- Treated AI systems as another performance channel with constraints and rules, not as magic.
You don’t need a new job title for this. You just need to accept that every campaign, landing page, and piece of content you ship is now doing double duty:
It sells to humans today. It trains the machines that will decide your reach tomorrow.