The real shift: you’re marketing to humans and models at the same time
Look across those headlines and a single pattern jumps out: the gatekeepers of attention are no longer just platforms and algorithms in the old sense. They’re generative models.
Search is now AI Overviews. Social feeds are co-written by recommendation models. Email deliverability is filtered by engagement models. Even “PR” is increasingly about whether LLMs cite you, summarize you, or skip you.
The new failure mode is already named: “People love it, models ignore it.”
That’s the problem that should keep CMOs and performance leaders up at night.
You’re not just building a brand in the minds of humans anymore. You’re building a brand in the weights of models.
Why this matters more than the next channel hack
A lot of the headlines you shared orbit the same gravitational center:
- AI Overviews stealing or reshaping search clicks
- Semantic search and brand-led SEO becoming the only SEO that matters
- Content that wins with readers but never gets surfaced by AI systems
- Social platforms pushing “recommended” content based on model trust
- AI’s trust problem in a SaaS recession and agency-client relationships under pressure
Underneath all that is one strategic question:
How do we design marketing that performs with humans and is legible and trusted by models?
If you don’t answer that, here’s what happens:
- Your search graphs look great, but pipeline doesn’t move because AI Overviews answer the question without sending traffic.
- Your social content gets bookmarked and shared by followers, but never enters the “recommended” flywheel.
- Your brand is famous in your category, but invisible in the tools people actually use to decide (ChatGPT, Perplexity, Gemini, CRM copilots).
Principle 1: Treat models as a distinct audience segment
Most teams still think in two layers:
- Humans: prospects, customers, influencers, analysts.
- Platforms: Google, Meta, TikTok, email clients, marketplaces.
You now have a third:
- Models: search LLMs, social recommenders, AI copilots, CRM/marketing AI, content filters.
Models have their own “buying criteria”:
- Can they parse what you do and for whom?
- Can they verify you via citations, consistency, and external signals?
- Can they map you cleanly to intents, entities, and outcomes?
- Do you look like spam, duplication, or hallucination-bait?
If you aren’t designing for that audience, you’re leaving performance on the table in every channel that now has an AI layer-which is basically all of them.
Principle 2: Make your brand machine-legible, not just memorable
“Brand-led SEO” and “semantic search” are just the visible tips of a deeper requirement: you need to be structurally understandable.
What machine-legible actually means
Think of your brand as a graph of entities and relationships that models can index:
- Clear entities: your company, products, features, key people, industries, and use cases are consistently named and described.
- Stable language: you don’t rename the same thing five different ways across site, sales deck, and PR.
- Structured context: schema markup, FAQs, comparison pages, glossaries, and internal links that show how concepts relate.
- External corroboration: third-party mentions, reviews, case studies, and citations that validate your claims.
This isn’t just “good SEO hygiene.” It’s how you train external models to understand when you’re the right answer.
Principle 3: Avoid the new content cannibalization trap
Old cannibalization: multiple pages fighting for the same keyword.
New cannibalization: you flood the web with near-duplicate takes, and models decide you’re noise.
The pattern:
- AI tools make it cheap to produce endless “Top 10” and “Ultimate Guide” content.
- Writers are briefed on volume, not distinctiveness or entity coverage.
- Models compress all that sameness into a generic answer that doesn’t need to cite you.
Net result: more content, less visibility, weaker brand signal.
Operator move: design a content map, not a content calendar
Before you brief another article or video, map:
- Core entities: the 20-50 concepts you must “own” (problems, solutions, categories, methods, frameworks).
- Coverage depth: for each entity, what exists today (on your site and off), and where you can add truly new information.
- Canonical pieces: which URL, video, or asset is the “source of truth” for each entity.
Then enforce a simple rule: no net-new content without a clear entity and a link back to its canonical home.
This keeps your brand’s “graph” clean, which models reward with clearer association and more confident inclusion in answers.
Principle 4: Build “model trust” the way you used to build domain authority
Several headlines hint at the same thing: “Does AI trust you?”, “Why AI makes agency-client relationships matter more than ever”, “AI’s trust problem.”
Underneath: models are constantly trying to decide who is credible, who is spam, and who is safe to recommend.
Signals that models likely treat as trust-building
You don’t control the black box, but you can stack the odds:
- Consistency over time: you’ve been publishing on a topic for years, not weeks.
- Cross-channel coherence: your site, socials, PR, and product all describe your value in compatible terms.
- Expert fingerprints: content with named experts, bios, credentials, and real-world data or case studies.
- Engagement that looks human: comments, discussion, and references that don’t resemble bot farms.
- Technical hygiene: fast site, clean markup, no obvious spam tactics, no weird cloaking or bait-and-switch.
For agencies, this is a positioning opportunity: “We don’t just get you clicks; we make your brand a safe, high-confidence answer for AI systems.”
Principle 5: Measure the right things in an AI-shaped funnel
“When search performance improves but pipeline doesn’t” is not a mystery. It’s a measurement problem.
AI layers are stealing or reshaping a chunk of the funnel that your dashboards still attribute to “direct,” “brand,” or nothing at all.
New metrics to track (even if they’re messy)
-
AI Overview presence: how often your brand, products, or content are:
- Mentioned
- Cited with a link
- Summarized without attribution
-
Model inclusion rate: for your top 50-100 buying questions, how often do LLMs (ChatGPT, Perplexity, Gemini) mention you when:
- Asked about your category
- Asked to compare vendors
- Asked for implementation advice
- Recommendation lift: in social, track the share of impressions from “recommended” or “For You” surfaces vs followers.
- Pipeline from “dark” discovery: add qualification questions like “Which tools or assistants did you use while researching this?” and trend the answers.
None of this will be perfectly attributable. It doesn’t need to be. It just needs to be consistent enough to guide budget and creative decisions.
Principle 6: Stop outsourcing your message to generic AI
The temptation in a budget-tight environment is obvious: use AI to crank out more content and copy with fewer humans.
The risk is also obvious: you train the internet (and therefore future models) that you’re indistinguishable from everyone else.
Where AI belongs in your stack-and where it doesn’t
Use AI for:
- Researching semantic fields and related entities you should cover.
- Summarizing your own expert content into different formats (email, social, sales enablement).
- Generating variants for testing subject lines, hooks, and angles.
- Auditing your site for consistency of terminology and structure.
Be very careful using AI for:
- Net-new “thought leadership” that doesn’t come from your operators or customers.
- Filling your blog with generic how-tos that add nothing new to the model ecosystem.
- Writing product messaging that blurs what actually makes you different.
In a world where models compress and summarize, differentiation is a survival trait. Don’t hand that away to the same tools your competitors are using.
Principle 7: Design campaigns that are “model-friendly” from day one
Think about your next big campaign-Super Bowl, category launch, pricing move, whatever. The usual checklist hits:
- TV, CTV, social, search, PR, influencer, email, site.
Add a new checklist: How will models see this?
Practical ways to bake in model visibility
- Canonical explainer: create a single, structured page that explains the campaign, the offer, and the key concepts in plain, consistent language.
- FAQ and schema: publish an FAQ that mirrors the questions users will ask AI assistants, and mark it up properly.
- Named concepts: if you’re introducing a new framework, feature, or term, name it clearly and use that name everywhere.
- Third-party validation: coordinate with partners, analysts, or customers to publish their own takes that link back and reinforce the same language.
- Post-campaign clean-up: after the spike, update evergreen pages to absorb what worked, retire what’s duplicative, and keep the graph tidy.
This is not extra work; it’s refocusing work you’re already doing so that it pays off in the places your dashboards don’t fully see yet.
What to do in the next 90 days
If you’re running marketing, media, or growth, here’s a concrete 90-day plan to get ahead of this shift:
1. Audit your machine legibility
- List your top 30-50 buying questions and intents.
- Ask major LLMs and AI search surfaces those questions. Document if and how you appear.
- Map your core entities and where they live on your site and key channels.
2. Fix the worst cannibalization
- Identify overlapping content on the same topic with similar intent.
- Consolidate into stronger canonical assets; redirect or clearly reposition the rest.
- Standardize naming for products, features, and use cases.
3. Rewrite one key narrative for humans and models
- Pick a high-value theme (e.g., “pricing discipline,” “loop marketing,” “AI CRM for X industry”).
- Create a single, deep, structured explainer with clear headings, glossary, FAQ, and examples.
- Align sales decks, ads, and social copy to the same language and claims.
4. Change how you brief content and creative
- Add explicit prompts in briefs: “Which entity does this strengthen?” and “What net-new information does this add to the ecosystem?”
- Require at least one proprietary element per piece: data, framework, story, or opinion.
- Ban briefs that exist solely to chase volume or keywords without a clear role in your entity map.
The operators who win the next three years will be the ones who accept a simple reality early: your real media plan is no longer just where you buy impressions. It’s how you train the systems that decide what anyone sees in the first place.