The real shift: you’re no longer just buying media, you’re feeding machines
Look past the usual noise-new LinkedIn algorithm tips, YouTube hook formulas, “100 most expensive keywords,” AI lead-gen hacks. The high-signal pattern is simpler and more brutal:
Your brand is no longer fighting only for positions on pages. It’s fighting to be included, trusted, and surfaced inside AI systems that increasingly sit between users and traditional media.
That’s what’s hiding inside:
- Google I/O and Marketing Live demos about AI Overviews and AI Max campaigns
- Pieces on “AI’s trust problem,” “citations in AEO,” and “AI content won’t fix rankings”
- Search results where “position 1 now appears halfway down the page”
- OpenAI working with Skai to bring retail and commerce advertisers into ChatGPT
The web is becoming agentic and AI-mediated. That breaks a lot of the muscle memory CMOs and media buyers rely on:
- Impression = user saw your ad or listing
- Click = user chose you
- Last-click or position-based models = “good enough” truth
In the AI-first web, the model often chooses for the user. Your job is to be the obvious choice to the model.
From SEO and PPC to AEO and “model share”
You’ve optimized for:
- SEO: organic rankings
- PPC: auction dynamics and Quality Score
- Paid social: feed algorithms and creative fatigue
The emerging layer is AEO: Answer Engine Optimization. But most AEO advice is still framed like SEO with a new hat. For operators, the more useful mental model is:
Your new KPI: model share.
What share of AI-generated answers, recommendations, and actions in your category include or prefer your brand?
That sounds fuzzy, but you can make it operational.
Three uncomfortable truths operators need to accept now
1. “Position 1” is now a suggestion, not a guarantee
With SERP layouts where position 1 appears halfway down the page and AI Overviews eating the top, your old “rank report” is a vanity metric. Visibility is being:
- Pre-filtered by AI (what gets summarized)
- Re-written by AI (how your offer is framed)
- Re-ranked by AI (what gets surfaced in follow-up questions)
If you’re still reporting “we’re #1 for [keyword]” without asking “are we in the AI answer at all?” you’re flying with the wrong instruments.
2. AI content is a commodity; AI-readable proof is the moat
Everyone has the same writing robot. The content gap is closing fast. The trust gap is widening.
That’s why you’re seeing:
- “AI content alone won’t fix your SEO rankings”
- “AI’s trust problem” and “citations matter more than backlinks for AI visibility”
- Case studies about 8,000 title tag rewrites and 37% inquiry lifts from conversion-focused site changes
Models are hungry for structured, corroborated, low-ambiguity signals. They need to know:
- What you actually do
- Where you do it
- Who you serve
- What proof exists that you’re any good
3. You are already in AI training sets-whether you planned for it or not
Publishers are quietly cutting six-figure AI licensing deals. Retail media data is flowing into LLMs. Product feeds and reviews are being ingested as “ground truth.”
You have two options:
- Be a passive data donor
- Design your presence so that when you’re ingested, you’re ingested correctly and advantageously
What this actually means for CMOs and media buyers in the next 12-18 months
You don’t need a 5-year vision deck. You need a 5-quarter operating plan. Here’s how to think about it.
1. Treat AI surfaces as channels, not curiosities
Anywhere an AI system can answer, recommend, or act on behalf of a user is now a channel:
- Google AI Overviews and AI Max campaigns
- ChatGPT / Claude / Gemini-style assistants
- Retail search and recommendation engines (Amazon, Walmart, Instacart, etc.)
- Social feeds increasingly tuned by AI (LinkedIn’s new feed, YouTube’s Shorts recommendations)
For each, define:
- Primary user intent: research, compare, decide, buy, troubleshoot
- Desired AI behavior: mention us, prefer us, explain us, troubleshoot us, recommend our SKUs
- Controllable inputs: feeds, schema, product data, content formats, reviews, bids, negative controls
2. Build an “AI visibility” dashboard, even if it’s ugly
You won’t get a clean API for “share of AI answer” anytime soon. You can still approximate it.
Start with a list of:
- Top 50-100 commercial intents in your category (from PPC keyword data and “most asked questions” style research)
- Top 20-50 brand and competitor queries
For each, track monthly:
- Are we mentioned in AI Overviews? Y/N
- Are we cited (link or brand mention) in AI answers in major assistants? Y/N
- Are our products or content used as examples or defaults?
- How often do competitors appear without us?
It’s manual, scrappy, and imperfect. It’s also better than pretending your search console report is reality.
3. Shift budget from “more content” to “better signals”
AI won’t reward you for having the 12th article on “what is X.” It will reward you for being the clearest, most corroborated source on:
- “How to choose X for [specific segment]”
- “What to do when X fails in [specific scenario]”
- “Real performance of X across [conditions / time / use cases]”
Reallocate a portion of your content budget to:
- Schema and structure: product schema, FAQ schema, how-to schema, organization schema
- Evidence assets: public case studies, benchmark pages, comparison tables, transparent pricing/feature matrices
- Canonical answers: tightly written, well-structured answers to the 50-100 most asked questions in your category
The goal: when models scrape and summarize, your answers are the easiest to reuse verbatim.
4. Train your team to think like a model, not a marketer
Three-quarters of CMOs are staring at an AI skills gap. Most training is focused on “how to use prompts.” That’s table stakes. What your team actually needs is:
- Basic model mental models: how LLMs use tokens, context windows, and retrieval
- Data hygiene discipline: how inconsistent naming, messy product feeds, and vague copy confuse models
- Evaluation habits: how to regularly test what models say about your brand and competitors
A simple weekly ritual that works:
- Pick 5-10 real customer questions from support or sales
- Ask 2-3 public AI assistants those questions, as a user would
- Document: Did we appear? How were we described? Who else showed up? What sources were cited?
- Feed the gaps back into your content, schema, and product data roadmap
5. Get ahead of AI-driven paid media controls
Google is testing new branded search controls in AI Max campaigns. OpenAI is partnering with retail and commerce platforms. These are early tells:
- AI front-ends will sell “sponsored suggestions” and “preferred providers”
- The line between performance and “AI-native brand presence” will blur
As a media buyer, you want three things:
- Negative controls: where you refuse to appear (e.g., competitor-branded queries, certain categories)
- Context controls: which intents you’re willing to pay to be the default answer for
- Attribution hooks: any parameter, ID, or redirect that lets you tie AI-sourced sessions to revenue, even roughly
Don’t wait for perfect tools. Start with:
- Separate campaigns for “AI surfaces” where possible
- Custom UTM schemes for AI-generated traffic
- Incrementality tests (geo splits, time-based holds) around new AI inventory
Practical moves you can make this quarter
If you run marketing, growth, or media, here’s a focused 90-day plan that doesn’t require reorgs or moonshots.
Week 1-2: Audit your AI footprint
- List your top 50-100 revenue-driving queries (search and internal site search).
- For each, check:
- Google: Are you in the AI Overview? Are you cited?
- One major assistant (ChatGPT, Gemini, or Claude): Do you appear in answers?
- Key retail/search partners: Do your products appear in recommended sets?
- Document the ugly truth in a simple sheet. No slideware.
Week 3-6: Fix your signals, not your slogans
- Standardize naming: products, plans, features, locations. Kill cute internal names that confuse machines.
- Implement or clean up schema on:
- Homepage and key category pages (Organization, Product, FAQ)
- Top 20 content pieces that answer real buyer questions
- Publish or refresh:
- At least 10-20 canonical Q&A pages mapped to real search queries
- 3-5 high-signal case studies with real numbers, clear segments, and explicit use cases
Week 7-10: Instrument and test AI-influenced traffic
- Create a separate channel grouping for “AI search / assistants” based on referrers and UTMs.
- Run a small, tightly scoped test on any available AI-native ad unit (AI Overviews, AI Max, or assistant integrations), with:
- Hard caps on spend
- Clear primary metric (lead quality, ROAS, or downstream revenue)
- A holdout region or time window to estimate lift
- Debrief ruthlessly: did it drive incremental value, or just cannibalize existing search?
Week 11-13: Turn “AI visibility” into an ongoing operating metric
- Assign ownership: one person responsible for maintaining the AI visibility sheet and running monthly checks.
- Add 2-3 AI visibility metrics to your regular marketing review:
- % of priority queries where we appear in AI answers
- # of competitor-only answers in our core category
- AI-influenced revenue (even if estimated)
- Use these to inform:
- Content roadmap
- Feed and schema work
- Paid search and retail media bidding strategies
The uncomfortable advantage
The AI-first web punishes brands that treat “being ingested by models” as an abstract risk or a legal problem. It quietly rewards the ones that treat it as an operational channel:
- Clean, consistent data instead of clever naming
- Evidence instead of slogans
- Visibility in answers instead of vanity rankings
You don’t need a grand AI narrative. You need to accept one simple fact: to win the next five years of marketing, media buying, and growth, you have to stop thinking only in terms of audiences and start thinking in terms of models.