The real shift: AI has become the default discovery layer
Scan those headlines and you see the same story from different angles:
- Google AI Mode, AI Overviews, user data shaping results
- ChatGPT being searched more than YouTube, Instagram, Facebook, TikTok
- AI bots getting blocked, domains suddenly “mattering” again
- Demand Gen, ChatGPT Ads, Apple adding more native ad slots
The through-line: AI is becoming the primary middleman between your brand and demand.
This isn’t “search vs social” anymore. It’s:
Human intent → AI layer → Platforms → Your brand (maybe).
If you run marketing, media buying, or growth, your job is now to:
- Be chosen by AI systems as the “safe, useful” answer
- Exploit new AI-native ad inventory before it commoditizes
- Rebuild your funnel for a world where discovery, consideration, and even purchase are compressed into a single AI interaction
From “rank on page 1” to “be the answer the model trusts”
SEO used to be simple in concept: get on page 1, then fight for clicks.
Now:
- Google AI Overviews summarize the web and may never show your blue link
- ChatGPT, Perplexity, Copilot, and others answer questions directly
- AI Overviews and “AI Mode” are increasingly personalized using user data
The game has changed from “rank higher” to “be the most quotable, safest source the model wants to use.”
How AI systems actually choose content
Different models, same incentives. They want:
- Low risk – no legal, medical, or brand-safety blowups
- High clarity – clean structure that’s easy to parse and summarize
- High consensus – aligned with other reputable sources
- High engagement signals – users don’t bounce, search again, or downvote
That means your “SEO” stack now needs to optimize for:
- Structure: headings, lists, tables, and explicit definitions that models can quote cleanly
- Evidence: data, citations, and clear methodology (especially for YMYL topics)
- Consistency: no wild contradictions across your own properties
- Brand safety: no borderline claims that scare a cautious AI system away
Operator move: design pages for AI extraction, not just human skimming
For your highest-intent topics, rebuild pages around:
- One canonical definition or answer high on the page, in plain language
- Step-by-step sections that can be lifted as instructions
- Short, self-contained paragraphs that still make sense when quoted alone
- Explicit “who this is for / not for” blocks – models love clear boundaries
Treat every key page as if an AI will:
- Read it once
- Strip away your design and context
- Copy 2-4 sentences into its answer
If those 2-4 sentences don’t stand alone as a strong representation of your POV, the page isn’t ready for the AI middleman era.
Performance marketing in an AI-first media stack
While SEO scrambles to adapt, paid is quietly shifting too:
- Google rolling out Demand Gen and AI-driven campaign types
- ChatGPT ads and AI-native placements emerging
- Apple adding more App Store ad slots, tightening its own walled garden
- Social platforms optimizing feeds around AI-generated and AI-filtered content
You’re not just bidding on keywords or audiences anymore. You’re bidding on:
“Show my brand when your AI thinks this person is ready for X.”
New media buying reality: trust scores and “AI comfort”
The question “Does AI trust you?” is not fluffy. It’s practical:
- If your domain has thin content and aggressive claims, you’re a liability to AI systems
- If your ad-to-landing experience feels bait-and-switch, models will see the bounce and adjust
- If your brand is unknown and your category is risky (health, finance, legal), you’re starting in a hole
Think of an informal “AI comfort score” with your assets:
- High comfort: clear, accurate, consistent, low complaint rates, strong dwell time
- Low comfort: ambiguous, over-claiming, inconsistent, high bounce or refund rates
Platforms won’t expose this, but they will act on it. Your CPMs and CPCs will quietly reflect whether the system is comfortable showing you as the answer.
Operator move: re-architect your paid stack around “AI-friendly” signals
Three concrete shifts:
-
Stop sending paid traffic to “good enough” pages
- Audit top landing pages for clarity, claims, and friction
- Strip out vague promises; add concrete proof and clear next steps
- Align headline, ad copy, and on-page copy so the journey feels inevitable, not jarring
-
Instrument the full path, not just the click
- Feed real post-click signals back into platforms: qualified leads, pipeline, LTV
- Use offline conversion imports and clean event schemas; AI bidding is only as good as what you feed it
- Kill campaigns where the model is clearly “confused” about who you serve (high impressions, noisy leads)
-
Test AI-native formats early, but with strict guardrails
- Experiment with ChatGPT-style conversational ads and Demand Gen, but cap budgets and define hard KPIs
- Don’t outsource your positioning to auto-generated ad copy; use AI for variants, not for strategy
- Document what the model seems to think you are, based on auto-suggestions and generated angles
Your funnel is now a loop, not a line
Several headlines talk about “Loop Marketing” and superfans. That’s not just a retention story. It’s an AI story.
AI systems increasingly rely on:
- User data and engagement history
- Brand and domain-level performance
- Signals from content sharing, mentions, and repeat visits
In other words, your existing customers are training the models on whether you’re a good answer.
Operator move: design your loop for AI-visible signals
Think of three loops you can actually influence:
-
Engagement loop
- Drive people back to owned content (site, app, community), not just social feeds you don’t control
- Make core content “habit-worthy” – recurring formats, updated benchmarks, live dashboards
- Use email and CRM not only to sell, but to get users repeatedly interacting with your best pages
-
Advocacy loop
- Turn your best customers into public signals: reviews, case studies, user-generated walkthroughs
- Encourage content on platforms AI crawls and respects (YouTube, blogs, forums, not just closed DMs)
- Make it stupidly easy to share “how I did X with [your product]” stories
-
Feedback loop
- Instrument where new customers say they heard about you: “Google AI answer,” “ChatGPT,” “Reddit,” etc.
- Run periodic “search yourself” audits: what do AI systems say about you vs your competitors?
- Feed what you learn back into content, product pages, and support documentation
Brand, “taste,” and why sameness is now actively punished
Another pattern in the headlines: everyone suddenly cares about “taste,” storytelling, and thought leadership again.
That’s not nostalgia. It’s math.
As AI tools make it trivial to produce mid-tier content and mid-tier creative, the average quality of the feed goes up while distinctiveness goes down.
The models then:
- Cluster generic content together as “more of the same”
- Reward content that drives unusual engagement or strong sentiment
- Use brand and creator-level performance as a ranking shortcut
So if your brand voice, visual identity, and POV look like they were assembled by committee and written by a polite chatbot, you’re not just boring. You’re invisible.
Operator move: bake “taste” into the brief, not the retro
You can’t A/B test your way into taste, but you can operationalize it:
- Create a “this is us / this is not us” reference board for creative and content teams
- Limit AI usage for final copy and visual concepts to guardrails and first drafts, not finished work
- Set one non-negotiable creative constraint per campaign (e.g., “no stock photography,” “no generic SaaS language,” “must include a concrete number or story”)
- Measure creative fatigue aggressively; if your best-performing ad is also your blandest, you’re training the models to associate you with generic performance
What to actually do in the next 90 days
If you’re a CMO, performance lead, or media buyer, here’s a pragmatic 90-day plan for the AI middleman era.
1. Run an “AI presence” audit
- Ask ChatGPT, Perplexity, and Google AI Overviews 10-20 core questions in your category
- Document:
- Which brands get mentioned
- Which URLs get cited
- What language is used to describe your category and competitors
- Flag any mismatches between how you want to be seen and how the models describe you
2. Design 3-5 “AI-quotable” pillar assets
- Pick your highest-value topics (by revenue, not just traffic)
- Rebuild those pages for:
- Clear definitions and summaries
- Structured data, tables, and lists
- Evidence, examples, and explicit “for / not for” sections
- Ensure they’re the canonical source you’d want an AI to quote for years
3. Clean your paid-to-landing experience
- Pull your top 10 paid ad groups or campaigns by spend
- For each, check:
- Does the landing page make a clear, defensible promise?
- Is the copy consistent with the ad and with your other pages?
- Are you seeing high bounce or low quality leads from specific platforms?
- Fix the worst offenders first; they’re actively training AI bidding systems against you
4. Instrument AI as a source of truth, not a black box
- Add “AI assistant / AI search” as a “how did you hear about us?” option
- Tag and track deals where buyers mention ChatGPT, Google AI, or similar
- Review this monthly and adjust your content roadmap to match real queries buyers are asking models
5. Decide your AI posture: creator, critic, or commodity
- Creator: you want to be the brand AI systems quote and recommend
- Critic: you openly challenge AI’s generic answers with sharper, contrarian POVs
- Commodity: you quietly ride the wave with low prices and convenience
All three can work. What doesn’t work is pretending AI is just another channel. It’s the new middle layer between every channel and your customer.
The operators who win the next few years won’t be the ones who shout the loudest. They’ll be the ones the machines quietly decide are safe to show, easy to quote, and satisfying to choose.