The shift nobody is naming clearly enough
Look at those headlines again and a pattern jumps out:
- AI Overviews and “AI Mode” studies
- Entity-based SEO and LLM-localized search
- Freshness, publish dates, and cannibalization
- AI assistants, chatbots, “AI employees”
The real story: we’re not in a “search” world anymore. We’re in an answer distribution world.
Google, ChatGPT, Perplexity, agentic browsers, AI assistants in OSes and TVs – they’re all doing the same thing:
intercepting demand and answering it directly before a click, a SERP, or an ad impression ever happens.
For performance marketers and media buyers, this isn’t a thought experiment. It’s a budget problem.
You’re still reporting on CPCs and ROAS while the surface area where decisions get made is quietly moving into AI layers you don’t control or measure well.
So the useful question isn’t “What’s the future of SEO or PPC?” It’s:
How do we buy and earn visibility in an AI-answers-first world?
The new funnel: from query → answer → brand slot
Old mental model:
- User types query
- Sees 10 blue links + ads + some SERP features
- Clicks around, compares, converts
New mental model:
- User asks a question (text, voice, image, chat)
- AI system composes a single answer from multiple sources
- Maybe shows 3-5 citations or product cards
- User often decides without deep clicking
Your job is no longer “rank #1” or “win the auction.” Your job is:
be the thing the answer engine feels safe and confident citing or recommending.
That requires a different operating system for growth.
Principle 1: Optimize for entities, not just keywords
LLMs and AI Overviews don’t “think” in keywords. They think in entities and relationships:
brands, products, people, locations, attributes, outcomes.
Practically, this means:
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Own your entity graph
Make sure your brand, products, and key concepts are consistently described across:- Your site (About, product pages, FAQs, docs)
- Structured data (schema.org, product markup, org markup)
- Third-party profiles (GMB, LinkedIn, Crunchbase, app stores, marketplaces)
- High-authority mentions (industry publications, review sites)
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Write for “What is X?” and “Is X good for Y?”
LLM answers lean heavily on definitional and comparative content:- “What is [your product category]?”
- “[Your brand] vs [competitor]”
- “Best [category] for [use case]”
- “Is [solution] worth it for [segment]?”
If you don’t publish this, someone else defines you – and the model will repeat their framing.
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Make your attributes machine-readable
LLMs love structured attributes when forming recommendations:- Price ranges
- Use cases (“for agencies”, “for solo founders”)
- Integrations (“works with Shopify, HubSpot”)
- Regions served
Put these in product schema, comparison tables, and consistent copy.
Don’t bury them in images or vague marketing lines.
Principle 2: Freshness is now a ranking and hallucination-control signal
Ahrefs is right to obsess over publish dates. Freshness used to be mostly an SEO lever.
Now it’s also an AI safety lever.
When an AI system answers:
- It prefers content that looks actively maintained (recent dates, updated schemas).
- It downweights obviously stale or abandoned pages.
- It uses “last updated” as a proxy for “less likely to be wrong or outdated.”
For performance teams, that means:
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Build a refresh calendar, not just a content calendar
Treat your top 50-100 pages like ad creatives:- Quarterly review for stats, screenshots, pricing, and feature changes.
- Explicit “Updated [Month Year]” labels.
- Changelogs for product and pricing pages.
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Stop spinning up near-duplicate pages
Cannibalization isn’t just an SEO problem now.
Multiple weak, overlapping pages confuse models about which URL is canonical or current.
Consolidate into fewer, stronger, clearly-maintained assets. -
Instrument content like you instrument ads
Track:- Which pages are being cited in AI Overviews / answer boxes (use manual checks + rank trackers that support it).
- Which refreshed pages see lifts in impressions, not just clicks.
You’re optimizing for visibility in the answer layer, not just traffic.
Principle 3: Treat AI surfaces as a media channel, not a black box
Right now, “AI Overviews” and assistant answers feel like organic-only territory. That won’t last.
But waiting for official ad products is a good way to get leapfrogged.
You can act like it’s a channel today:
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Map your “AI answer SERPs”
For your top commercial queries:- Search them in Google with AI Overviews on.
- Ask them in ChatGPT, Perplexity, Claude, and other assistants your audience uses.
- Screenshot the answers, citations, and recommended products.
That’s your new competitive set. It’s not just “who ranks”; it’s “who gets name-checked in the answer.”
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Reverse engineer answer inclusion criteria
Look at the pages that get cited:- Are they long-form guides, comparison tables, docs, PR, reviews?
- Do they use certain phrases (“according to [brand]”, “research shows”)?
- Do they have strong schema, clear headings, or specific formats (FAQs, how-tos)?
Then rebuild your own content to match the patterns that clearly get pulled.
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Use paid to seed the signals organic AI needs
This sounds backwards, but it works:- Run paid campaigns (search, social, native) to drive attention and links to the exact assets you want AI to cite.
- Promote third-party reviews, case studies, and comparisons that mention you by name.
- Use UGC and influencer content to create “evidence” that models can see and repeat.
You’re effectively buying the early data that trains the organic answer ecosystem.
Principle 4: Brand is now a performance input, not a separate budget
The Adweek and Social Media Examiner pieces hint at this: “brand-led growth beats performance,”
“death of organic reach,” “marketing loves declaring the end of things that still work.”
In an AI-answers world, brand strength literally changes the answer.
- Models are more likely to recommend brands they “see” everywhere.
- They treat well-known brands as safer, less risky defaults.
- They echo consensus: “X is widely considered the leading…”
So for performance leaders, brand isn’t a vanity line item. It’s how you:
- Increase your odds of being the default recommendation.
- Reduce your effective CAC over time as “brand” handles more of the persuasion.
- Defend margin when auctions get more expensive and organic clicks shrink.
Practically, that means:
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Buy reach that leaves a data trail
Favor channels and formats that:- Get indexed (YouTube, podcasts with transcripts, PR, high-authority blogs).
- Generate structured mentions (review sites, marketplaces, app stores).
- Produce quotable lines and stats that models can reuse.
A TV spot with no digital footprint helps humans, not models.
A TV spot that spawns PR, YouTube breakdowns, and social commentary helps both. -
Engineer “answer hooks” into your brand story
Give AI a simple way to summarize you:- “[Brand] is the [positioning] for [segment] that [unique proof].”
Repeat this everywhere. Models will converge on the shortest, clearest description they see most often.
Principle 5: Build AI-native conversion paths, not just AI-aware top-of-funnel
Most teams are thinking about AI at the awareness and research stage.
Fewer are rebuilding conversion for an AI-mediated world.
If discovery and consideration happen inside AI layers, your site’s job shifts:
- Less “educate from zero,” more “confirm and remove friction.”
- Less “here’s what we do,” more “yes, what the AI told you is correct and here’s how to start now.”
Tactically:
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Design “answer-aware” landing pages
Assume the visitor already saw a summary in an AI answer:- Open with a tight confirmation: “If you’re looking for [X described in AI answer], you’re in the right place.”
- Address the 2-3 most likely objections immediately (price, complexity, risk).
- Use concise, scannable proof: case studies, logos, quantified outcomes.
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Use your own AI to compress friction
Not “cute chatbot,” but:- Guided product finders that ask 3-5 smart questions and recommend a plan.
- Sales assistants that summarize long docs, contracts, or proposals for prospects.
- On-site Q&A trained on your docs, not the whole internet.
The bar is: Is this faster and more precise than the generic AI they already used before clicking?
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Instrument AI-influenced conversions separately
Add “How did you first hear about us?” with options like:- “AI assistant (ChatGPT, Perplexity, etc.)”
- “Google AI Overview / search answer box”
It’s imperfect, but you need a rough sense of how much revenue is coming from AI surfaces
to justify the work you’re doing upstream.
A simple operating plan for the next 12 months
If you run growth, media, or performance and you want something you can actually execute, here’s a stripped-down plan.
Quarter 1: Visibility audit and entity cleanup
- List your top 50-100 revenue-driving queries and topics.
- For each, capture:
- Traditional SERP (who ranks, who runs ads).
- AI Overviews / answer boxes (who’s cited).
- Assistant answers in 2-3 major LLMs (who’s mentioned).
- Fix the basics:
- Organization and product schema on key pages.
- Consistent brand and product descriptions across your site and major profiles.
- Consolidate obvious cannibalized content.
Quarter 2: Build answer-friendly assets
- Create or overhaul:
- “What is [category]?” and “[Brand] vs [competitor]” pages.
- Use-case pages (“for agencies,” “for ecommerce,” etc.).
- Data-backed resources (benchmarks, studies, original stats) that models can quote.
- Implement a refresh calendar for top pages (at least twice a year).
- Start tagging and tracking AI-cited pages in your analytics stack.
Quarter 3: Use paid to reinforce organic AI signals
- Promote your answer-friendly assets via:
- Search ads on high-intent queries.
- Paid social to target segments that match your use-case pages.
- Native / content syndication for data-heavy pieces.
- Run PR and partnership campaigns to earn authoritative mentions that repeat your positioning.
- Push for inclusion in category roundups, review sites, and comparison articles.
Quarter 4: AI-native conversion and measurement
- Redesign 3-5 key landing pages to assume “AI-prequalified” visitors.
- Deploy at least one AI-powered conversion tool (guided quiz, sales assistant, or on-site Q&A) that actually reduces time-to-decision.
- Standardize “AI-influenced” attribution questions in your forms and sales calls.
- Re-run your Q1 visibility audit and compare:
- Share of citations in AI answers.
- Brand mentions in LLM responses.
- Revenue share from self-reported AI-influenced leads.
The platforms will keep changing names and formats – AI Overviews, agentic browsers, assistants baked into everything.
The underlying game is stable:
become the safest, clearest, most consistently described answer in your category.
That’s the new performance marketing.