The shift no one budgeted for: search is now an AI surface
The most important change in marketing right now is simple and brutal:
search is no longer a list of links; it’s an AI answer layer that sits between you and your customers.
Google AI Overviews, AI Mode, semantic search, OpenAI’s ad product, social search on Threads and Bluesky – all the headlines are pointing at the same thing:
distribution is being intermediated by AI systems that decide what to show, what to cite, and who gets the click.
If you’re still running a “rank on page 1, bid on keywords, retarget the click” playbook, you’re already behind. The game has changed in three ways:
- Search results are answers, not just links.
- AI recommendations change with nearly every query and user.
- Visibility is now about semantic fit and model trust, not just keywords and bids.
From keywords to concepts: semantic search is the new targeting
AI Overviews and chat-style search don’t care about your exact-match keyword density. They care about:
- What topic you’re truly about.
- How consistently you cover that topic across your site and content.
- Whether other credible sources reference you for that topic.
This is semantic search: models map queries to concepts, then match concepts to entities (brands, people, properties) and content. Your job is to become an obvious answer to a clearly defined set of concepts.
The old model vs. the new model
Old search model:
- Target keywords.
- Optimize on-page factors.
- Acquire links.
- Measure rankings and clicks.
New AI search model:
- Own specific problem spaces and entities (you, your product, your category).
- Publish content that answers edge cases and real-world use, not just head terms.
- Earn semantic endorsements: citations, mentions, co-occurrences in trusted sources.
- Measure inclusion in AI answers, not just blue-link rankings.
What AI is actually optimizing for (and how to feed it)
Models don’t “read” your site like humans. They infer patterns. If you want to show up in AI Overviews, ChatGPT answers, or social search, you need to give the models the right signals.
1. Topical authority, not random coverage
Spreading content across 50 unrelated topics makes you invisible in AI. You’re not a clear answer to anything.
Operator move: pick 3-7 core problem spaces you want to own. For each:
- Map the journey: beginner questions, in-depth comparisons, implementation, troubleshooting, ROI.
- Build a structured content cluster: pillar pages, supporting articles, tools, FAQs, case studies.
- Cross-link them in a way that makes the topic tree obvious to both users and crawlers.
2. Structured, machine-readable context
AI systems love structure because it’s easy to parse and reuse. If your content is just walls of prose, you’re leaving visibility on the table.
Operator move:
- Use schema markup aggressively: product, FAQ, how-to, organization, author, review, event.
- Turn key insights into lists, tables, steps, and Q&A blocks.
- Standardize terminology for your product, category, and use cases so models see consistent patterns.
3. External signals of trust (beyond backlinks)
“Does AI trust you?” is not a fluffy question. Models are trained on the open web. They learn:
- Which brands get cited as sources.
- Which experts are quoted in credible outlets.
- Which domains are associated with specific concepts.
Operator move:
- Shift part of your PR from “vanity coverage” to semantic PR: placements that explicitly tie your brand to your target topics.
- Push for exact phrasing in coverage: “<Brand> is a <category> platform that helps <audience> do <job>.”
- Get your experts quoted on the same topics you want to rank for in AI answers.
The invisible problem: AI cannibalization and “missing” traffic
AI Overviews and answer boxes are already stealing clicks you used to get. The scary part: most analytics setups don’t show you what you lost.
You see “organic still looks okay” and assume the damage is minor. Meanwhile:
- Your high-intent informational queries are being answered directly in AI Overviews.
- Your brand is sometimes cited – but the user never clicks through.
- Your category competitors are being recommended in the same answer box.
How to diagnose AI cannibalization
Step 1: Identify AI-prone query classes
- “How to…” and “best way to…”
- “Top <category> tools / platforms / software”
- “What is <concept>” and “<concept> examples”
- “Is <product> worth it / legit / safe”
These are the queries most likely to trigger AI Overviews, comparisons, and recommendations.
Step 2: Track AI presence and your inclusion
- Use SERP tracking tools or manual checks to log:
- Whether an AI Overview appears.
- Which brands are mentioned or cited.
- Whether your domain is linked, mentioned, or missing.
- Tag affected queries in your reporting as “AI impacted.”
Step 3: Correlate with traffic and conversion shifts
- Compare pre- and post-AI Overview periods for those queries:
- Impressions vs. clicks (via Search Console).
- Landing page sessions and conversion rate.
- Look for:
- Flat or rising impressions with declining clicks.
- Stable traffic but worse lead quality (AI is filtering for different intent).
This isn’t academic. This is how you justify reallocation of budget from “rankings at all costs” to “being the cited answer in AI.”
Designing content for AI Overviews and answer surfaces
“How to rank in AI Overviews” is the wrong question. The right one is:
how do we become the easiest brand for AI to use as a source?
Make your content answer-shaped
AI Overviews and chatbots like to:
- Summarize steps.
- Compare options.
- List pros/cons.
- Quote definitions.
Operator move: for every high-intent topic page:
- Open with a clean, quotable definition in 1-3 sentences.
- Include a clear step-by-step process or checklist.
- Add a comparison table if alternatives exist.
- Include a short “Key takeaways” block with 3-5 bullets.
You’re not just helping users skim; you’re giving models reusable blocks they can lift and cite.
Own the “brand + problem” narrative
AI systems will increasingly answer questions like:
- “Is <Brand> good for <use case>?”
- “Alternatives to <Brand> for <audience>.”
Operator move:
- Create pages and help docs that explicitly address:
- Who your product is for and not for.
- Best use cases and bad fits.
- Comparisons to adjacent categories (not just direct competitors).
- Use natural language that mirrors user questions:
“Is <Brand> right for agencies?”, not just “Agency pricing.”
This is where “client fit” thinking from PPC actually becomes an AI visibility tactic. When you state your fit boundaries clearly, you give models strong signals about when to recommend you – and when not to.
Media buying in an AI-first discovery world
This isn’t just an SEO story. Paid media is being pulled into the same gravity well.
Google is testing third-party endorsements in search ads. OpenAI is rolling out high-minimum ad products. Social platforms are pushing “recommended” content into feeds based on engagement and semantics, not just follows.
What changes for performance marketers
1. Creative and messaging must be model-readable
- Use clear, descriptive language about what you are and who you’re for.
- Stop relying on clever-but-vague taglines that tell models nothing.
- Align ad copy, landing pages, and on-site messaging so models see one consistent story.
2. Brand and performance are now the same game
AI systems don’t care about your org chart. They care about:
- How often your brand is mentioned in positive, credible contexts.
- How strongly you’re associated with specific problems and outcomes.
- How users react after exposure (click, dwell, bounce, convert).
That means “brand campaigns” that generate mentions, searches, and citations are now direct inputs into your performance surface in AI answers and recommendations.
3. Measurement must move beyond last-click and channel silos
In an AI-intermediated world, a user might:
- See your brand in a YouTube ad.
- Ask ChatGPT for alternatives later.
- Click a competitor from an AI Overview.
- Eventually convert via a branded search ad.
Your analytics will credit “brand search” and call it a day. The AI layer that diverted or delayed the sale is invisible.
Operator move:
- Track brand search volume, brand mentions, and AI citations as leading indicators, not vanity metrics.
- Use media mix modeling or at least structured testing to understand how upper-funnel and PR affect AI-era performance.
- Stop treating SEO, paid search, and PR as separate sports; they now compete and collaborate on the same AI surface.
What to actually do in the next 90 days
This shift is big, but the first moves are not complicated. They are operational.
For CMOs
- Ask for an “AI Surface Audit”:
- Where do we appear in AI Overviews, chat answers, and social search?
- Where do we not appear but should?
- Reframe goals from “rankings and impressions” to:
- Share of AI answers for key problems.
- Brand + problem association strength.
- Fund a cross-functional squad (SEO, content, PR, paid) with a single mandate: increase our presence as a cited, recommended answer.
For performance marketers and media buyers
- Audit your top 50 non-brand queries and ad groups:
- Which trigger AI Overviews or heavy recommendation units?
- Where are you missing from the organic or AI layer?
- Rewrite key ad and landing page copy to:
- State clearly: category, audience, primary outcome.
- Mirror the natural language of the queries you see in search terms and social comments.
- Set up basic tracking for AI presence (manual or tool-based) and review it alongside your weekly performance dashboards.
For growth leaders
- Stop treating AI as a “tooling” topic and start treating it as a distribution topic.
- Push your teams to define:
- The 5-10 problem spaces you want to be the default answer for.
- The entities (brand, founders, flagship products) you want models to recognize and recommend.
- Measure progress not just in pipeline, but in:
- Share of voice in AI answers.
- Growth in branded and “brand + problem” queries.
- Quality of mentions and citations across the web.
The AI layer between you and your buyers is already here. You can either complain about lost clicks, or become the brand the models can’t ignore.