The real shift isn’t “AI in marketing.” It’s AI as the interface.
Scan those headlines and there’s a pattern hiding in plain sight:
- “AI Overviews” and “AI Mode” studies
- “AI search strategy” and “entity-based SEO” explainers
- “Localized SEO for LLMs” and “fresh content for AI visibility”
- “The Death of Organic Reach: What Works Right Now”
Everyone is still talking about channels. But the real change is the interface.
Users are no longer just searching or scrolling. They’re asking systems:
“What’s the best X for me, right now?” And the system is answering once.
For performance marketers and media buyers, this is the actual problem:
you’re still optimizing for a web of blue links and social feeds, while
the user is increasingly seeing a single synthetic answer, a single
product carousel, or a single AI recommendation.
This isn’t “SEO vs PPC” or “brand vs performance.” It’s:
How do I become the default answer in an AI-mediated world?
What AI surfaces are doing to your funnel (whether you like it or not)
Let’s define what’s happening in practical terms.
1. AI is compressing consideration into a single surface
AI Overviews, product recommendation blocks, chat-style results, TikTok
search, Instagram “For You,” Amazon’s “AI product highlights” – these all
do the same thing: they compress the messy middle of research into one
curated surface.
Historically, your growth engine assumed:
- Multiple search results → multiple clicks → retargeting → nurture
- Multiple social impressions → follow → DM/remarketing → conversion
Now, more often:
- User asks → AI answers → user taps one of a few surfaced options
Fewer touches. Fewer chances to “win them back.” More power in the surface.
2. Organic reach is dying for humans, not for machines
Social Media Examiner is writing about the “death of organic reach.”
SEO blogs are obsessed with “fresh content” and “publish dates” for AI visibility.
The nuance: organic reach to humans is down. But organic reach to
machines is the new game. LLMs, ranking systems, recommendation engines
– they’re your real readers now.
You’re not just publishing for people. You’re feeding models that will later
summarize, recommend, and rank on your behalf.
3. “Brand vs performance” is the wrong fight
Adweek is pushing “brand-led growth beats performance marketing.”
At the same time, PPC and SEO outlets are still obsessing over title tags,
cannibalization, and AI WordPress plugins.
In an AI-mediated world, brand and performance collapse into a single question:
Does the system trust you enough to surface you?
That “trust” is built with:
- Entity strength (who/what you are in the graph)
- Behavioral performance (do people click, stay, buy, return)
- Consistency of signals (content, reviews, links, mentions)
Call that “brand.” Call it “performance.” The label doesn’t matter. The
system is measuring it either way.
The operator’s problem: you’re still playing a channel game
If you’re a performance marketer or media buyer, your dashboards are probably:
- Google Ads / Meta Ads / TikTok Ads
- SEO traffic and rankings
- Attribution models and blended CAC
None of those directly tell you:
- How often you’re the default answer in AI surfaces
- How strong your entity is compared to competitors
- How “machine-readable” and “machine-preferable” your brand is
You’re optimizing levers that matter inside channels, while the
real battle is happening one layer above the channel – in the AI interface.
From channels to surfaces: a new operating model
Here’s a practical way to reframe your work without burning everything down.
Step 1: Map your critical AI surfaces
Don’t start with “AI strategy.” Start with: where are AI systems already
mediating your category?
Examples by vertical:
-
Ecommerce: Google AI Overviews, Amazon search and “AI highlights,”
TikTok search, Instagram Shopping, price-comparison engines. -
B2B SaaS: Google AI Overviews, Bing Copilot, ChatGPT browsing,
niche review sites with AI summaries (G2, Capterra), LinkedIn feed/search. -
Local / services: Google AI Overviews, Maps, Apple Maps,
Yelp/Tripadvisor AI summaries, local discovery via TikTok/Instagram.
For each surface, answer:
- What exact queries or intents matter most for revenue?
- How is the surface currently answering them?
- Which brands show up repeatedly? Are you one of them?
This is your “AI share of surface.” Treat it like you treat share of voice.
Step 2: Treat your brand as an entity, not just a domain
Entity-based SEO isn’t academic anymore. It’s how LLMs decide who you are
and when you’re relevant.
Practically, this means:
-
Consistent naming: Brand, product names, categories, and key people
should be described the same way across your site, profiles, and major directories. -
Structured data: Use schema (Organization, Product, FAQ, HowTo,
LocalBusiness, Person) so machines don’t have to guess. -
Contextual mentions: Get coverage and citations that tie you to
the right topics and categories, not just random backlinks. -
Own your “about” story: Have a clear, detailed About page that
spells out who you serve, what you do, where you operate, and how you’re different.
LLMs scrape and summarize this.
Your goal: when an AI tries to answer “Who is [Brand] and what do they do?” it
doesn’t hallucinate or shrug. It has a clean, confident answer.
Step 3: Optimize for “answer fit,” not just keyword fit
Traditional SEO: “What keywords do we want to rank for?”
AI-surface reality: “What questions do we want to be the answer to?”
Build a simple “answer map”:
- List your top 20-50 revenue-driving questions users actually ask.
- Group them by intent: educate, compare, decide, troubleshoot.
- For each, define: what’s the best answer we could possibly give?
Then:
-
Create or refactor content around those questions, not just keywords.
Use clear H2/H3 questions and direct, concise answers. -
Add FAQs with literal user questions and punchy answers – these are
catnip for AI snippets and summaries. -
Keep timestamps and update cycles sane. Ahrefs is right: publish dates
and freshness signals are now ranking signals for AI visibility.
You’re training models to see you as the canonical answer for specific intents.
Step 4: Make your performance data machine-friendly
AI systems increasingly use behavioral signals as a proxy for quality.
That means your “conversion rate optimization” work is now also “AI preference optimization.”
Focus on:
-
Fast, clean pages: Slow, cluttered pages get worse engagement,
which feeds back into ranking and recommendation systems. -
Clear primary actions: If users land and bounce because they
can’t figure out what to do, you’re teaching systems that your page is a bad answer. -
Consistent messaging: If your ad promise and landing page
content don’t match, you tank engagement and, over time, trust.
You’re not just improving ROAS; you’re improving your odds of being the
recommended option next time.
Step 5: Buy your way into the surfaces that matter
This is still MarketingPro, not a meditation blog. You have budgets. Use them.
The trick is to stop thinking of media purely as “traffic” and start thinking
of it as “signal injection” into the systems that generate AI answers.
Tactically:
-
Search & shopping ads: Use paid to force exposure and
engagement on the queries you want to own organically and in AI Overviews. -
Paid social for entity reinforcement: Run campaigns that
repeatedly tie your brand to specific problems, categories, and audiences. -
Retail media & marketplaces: For ecommerce, treat
Amazon/Walmart/TikTok Shop ads as ways to train those ecosystems that
your products convert well for certain intents.
Media buying becomes a way to accelerate the feedback loops that AI systems use.
What to change in your reporting and planning, starting this quarter
You don’t need a 50-slide “AI strategy deck.” You need a few new metrics
and a different way to ask questions in your weekly reviews.
New metrics to track
-
AI share of surface: For your top 20-50 intents, manually
(or via tools) check how often you appear in AI Overviews, product
recommendations, or top answer blocks. -
Entity coverage score: Audit key directories, review sites,
knowledge panels, and social bios for consistency of name, category,
and description. Track % of “clean” vs “messy” entries. -
Answer depth: For each critical question, rate your
current content from 1-5 on “would an AI reasonably choose this as the
best available answer?” -
Surface-driven revenue: Where possible, tag and track
conversions that originate from AI-like surfaces (AI Overviews, product
recommendation blocks, marketplace “top picks,” etc.).
Questions to ask in your growth meetings
- Where are AI systems already mediating discovery in our category?
- For which intents are we clearly the default answer? Where are we invisible?
- What signals are we sending that would make a model not trust us?
- Which campaigns this month also strengthen our entity and answer footprint?
- What’s one surface we’ll deliberately optimize for over the next 90 days?
What this means for your role as a performance operator
The job is shifting from “buying attention in channels” to
“engineering preference in systems.”
Concretely, that means:
- You still run ads – but you also think about how those ads train algorithms.
- You still do SEO – but with entities and answers, not just keywords and links.
- You still care about brand – but as a measurable, machine-readable asset.
The web you learned on was about pages and placements. The web you’re
operating in now is about surfaces and systems.
Stop optimizing for a web that no longer exists. Start asking a better question:
“If an AI had to pick one answer here, how do we make sure it’s us?”