The real shift: AI is becoming the homepage of the internet
Look at those headlines and a pattern jumps out: everyone is quietly admitting the same thing.
AI layers are sitting between your brand and your customer.
Not “coming soon.” Already here:
- AI Overviews and AI Mode on Google
- ChatGPT, Perplexity, Claude, and friends
- Social feeds that summarize, remix, and bury links
Search, social, and even email are being rewritten by systems that:
- Answer instead of referring
- Summarize instead of sending traffic
- Recommend brands instead of ranking pages
For performance marketers and media buyers, this isn’t a thought experiment.
It’s a routing problem: your traffic is being intercepted.
The question is no longer “How do I rank?” or “How do I go viral?”
It’s: “How do I become the answer the AI gives, and how do I still capture demand when the click never happens?”
The AI interception problem in plain language
Three big shifts are hitting operators at the same time:
- AI answers are cannibalizing clicks. Users get what they need from the answer box or chat thread.
- Brand visibility is being re-scored. LLMs decide which brands are “credible” enough to mention.
- Ad inventory is being rebuilt around AI interfaces. OpenAI, Google, Meta are all moving to ad-driven AI surfaces.
The old playbook assumed:
- Search = 10 blue links + some ads
- Social = posts that push people to your site
- SEO = rank a page, get a click, convert
The new reality:
- Search = 1 synthesized answer + a couple of links that may or may not be yours
- Social = AI-assisted creation and AI-assisted consumption
- SEO = be the source, be the brand mentioned, or be invisible
What actually matters now: being “AI-visible” and “AI-convertible”
Two concepts matter more than any trend list:
- AI visibility: How often and how prominently your brand is named or cited in AI answers.
- AI convertibility: How effectively you turn the smaller number of clicks you still get into revenue.
You can’t fully control the first, but you can influence it.
You absolutely control the second, and most brands are under-invested there.
AI visibility: how to become the brand the model reaches for
LLMs don’t “rank” pages the way Google’s classic algorithm does, but they still need:
- Clear, structured information
- Signals of authority and trust
- Brand mentions across the web
That means your practical job is to make your brand:
- Easy to summarize
- Safe to recommend
- Frequently referenced
1. Structure content for machines, not just humans
AI systems love content that looks like documentation:
- Clear headings that map to questions (Who is this for? How does it work? Pricing? Limitations?)
- Short, direct definitions and explanations
- Tables, bullets, and comparison sections
For your high-intent pages (product, category, core “how it works” content), ask:
- Could an LLM easily lift a clean, accurate answer from this page?
- Is the brand name tightly coupled with the problem and the solution?
- Are we explicit about what we do not do, so the answer is safe to reference?
You’re not just writing for users anymore.
You’re writing for the AI that will rewrite you.
2. Make your brand the “safe default”
AI systems are risk-averse. They prefer:
- Brands with strong third-party coverage
- Brands with clear, up-to-date information
- Brands that look “established” in their niche
Practically, that means:
- PR and thought leadership with a purpose. Not vanity, but coverage that clearly ties your brand to a category or use case.
- Freshness discipline. Update key pages and show dates. AI systems and search engines are both weighting recency harder.
- Category naming consistency. Pick a category label and stick to it across your site, profiles, and PR. Models learn patterns.
3. Treat AI surfaces like new “SERPs” and monitor them
You already track:
- Classic SERP positions
- Share of voice on key keywords
- Impressions and CTR in ad platforms
Add a new layer: AI answer visibility.
Operationally:
- Build a list of your top 50-100 commercial queries.
- Regularly test them in:
- Google AI Overviews / AI Mode
- ChatGPT (with browsing / search plugins where relevant)
- Perplexity or other meta-search tools your audience might use
- Record:
- Is your brand mentioned?
- Which competitors are?
- Which sources are being cited?
This becomes your “AI share of answer” baseline.
It won’t be perfect, but it will show you where you’re invisible.
AI convertibility: if clicks are down, every visit has to work harder
Even if you win more AI visibility, you will likely get fewer clicks per impression than in the old world.
That makes two things non-negotiable:
- Your landing experiences have to be ruthless about clarity and friction.
- Your remarketing and lifecycle flows have to catch the people who don’t convert on the first touch.
4. Build “answer-grade” landing pages
Think of your top pages as competing with AI answers, not other websites.
That means:
- Lead with the answer. Above the fold, state exactly what you do, for whom, and the primary outcome. No vague slogans.
- Mirror the query. If the query is “best CRM for agencies,” your page should explicitly address “CRM for agencies,” not just “modern CRM for teams.”
- Compress the path to value. Fewer fields, fewer steps, more clarity on what happens next.
You’re not just trying to beat competitors.
You’re trying to beat the user’s instinct to stay in the AI interface.
5. Fix the 73% of broken journeys you’re ignoring
That headline about “73% of your ecommerce emails are broken” is a symptom.
Most brands obsess over acquisition and then run:
- Outdated onboarding sequences
- Generic cart/browse abandonment flows
- Irrelevant product recommendations
In an AI-intercepted world, retention and lifecycle are where your margin lives.
Actionable moves:
- Audit your triggered flows quarterly. Don’t just check if they send; check:
- Is the message still accurate?
- Is the offer still aligned with your pricing and positioning?
- Are we using behavior (pages viewed, categories, time on site) to segment?
- Use AI where it’s strong: variation, not strategy. Let models draft alternative subject lines, intros, and product blocks, but keep the logic and the promise human-controlled.
- Measure by incremental revenue, not open rate. You’re optimizing a system, not a single email.
Paid media in the AI era: from audiences to intent fabrics
Google is lowering audience size limits.
Meta keeps collapsing targeting options.
AI is doing more of the matching behind the scenes.
The practical shift: you’re moving from micromanaging audiences to:
- Feeding the platforms better signals
- Designing creative that maps to real intents and stages
- Letting the system find the pockets of performance
6. Treat creative as your primary targeting layer
If the platforms are compressing audience controls, your creative has to carry more of the sorting job.
Think in terms of intent-coded creatives:
- Problem-aware: Ads that name the pain and describe the category.
- Solution-aware: Ads that compare approaches or vendors.
- Product-aware: Ads that handle objections, proof, and specifics.
Then:
- Map each creative set to matching landing experiences.
- Use broad or simplified targeting, but segment reporting by creative theme.
- Let the algorithm optimize delivery, you optimize the “who is this for and what is it promising?” layer.
7. Build your own “AI-ready” first-party data
AI-driven ad systems reward advertisers who can send back clean, meaningful signals:
- Standard events (purchase, lead, add to cart, etc.)
- Custom events that reflect real value (qualified lead, high-LTV cohort, subscription start)
- Offline conversions stitched back to ad exposure
Your job:
- Define what “good” looks like. Not just “any lead,” but leads that close, customers that retain.
- Instrument those events properly. Pass them back via pixels, APIs, and offline conversion uploads.
- Use these events as optimization goals. Let the systems train on value, not vanity.
This is how you stay competitive when everyone has access to the same AI-powered bidding.
Owning something AI can’t easily copy: brand and narrative
Another pattern in the headlines: the ads that “made people feel something” are still winning.
AI can summarize information, but it’s still clumsy at owning a point of view.
For performance teams, “brand” often sounds like a cost center.
In an AI-first distribution world, it’s an insurance policy:
- People search your brand name, not just the category.
- They scroll past generic AI content because they recognize you.
- They trust your claims more than a synthesized paragraph.
8. Make your POV part of the product
Concretely:
- Pick a sharp stance. “We’re the fastest,” “We’re the most transparent,” “We’re built for X niche.” Not all three.
- Repeat it everywhere. Ads, landing pages, onboarding, sales decks, support scripts.
- Back it with proof. Benchmarks, case studies, side-by-side comparisons.
This gives AI systems a simple, repeatable way to describe you.
It also gives humans a reason to care.
How to operationalize this in the next 90 days
If you’re running performance today, here’s a realistic 90-day plan:
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Week 1-2: Map the interception points.
- List your top 50-100 revenue-driving queries and social topics.
- Check how they appear in Google AI Overviews, ChatGPT, and one meta-search tool.
- Score: brand mentioned, not mentioned, or misrepresented.
-
Week 3-6: Fix the obvious leaks.
- Rewrite 5-10 core pages to be “answer-grade” (clear, structured, up-to-date).
- Audit and update your main lifecycle flows (welcome, abandonment, post-purchase / post-signup).
- Standardize event tracking and make sure your top value events are passed back to ad platforms.
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Week 7-10: Rebuild your creative system.
- Define 3-5 core intents and stages for your audience.
- Create creative sets for each, with matching landing experiences.
- Shift more budget into campaigns optimized for high-value events, with broader targeting and sharper messaging.
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Week 11-13: Start measuring “AI share of answer.”
- Re-run your AI visibility checks.
- Track any changes in brand mentions and traffic quality.
- Feed learnings back into content, PR, and product marketing.
The operators who win this cycle won’t be the ones who guess the most AI trends.
They’ll be the ones who treat AI as a new distribution layer, retool their funnels for fewer but higher-intent clicks, and build brands that are easy for both humans and machines to choose.