The real story behind all the AI headlines
Strip away the hype and the recent headlines are all pointing at one thing:
your distribution is being quietly rewritten by AI systems you don’t control.
Google’s AI Overviews, semantic search, AI-powered SEO tools, AI images, AI CRM, AI analytics, AI ad products – and now even paid placements inside ChatGPT.
Different surfaces, same problem:
Your brand is increasingly mediated by AI recommendation layers that decide what gets seen, trusted, and clicked – often instead of the open web.
For CMOs, performance marketers, and media buyers, this is not a thought experiment.
It’s a budget allocation problem and a measurement problem.
If you keep planning like it’s “10 blue links + social feeds,” you’ll quietly bleed demand into systems that never show you the loss.
The new funnel: human demand, machine gatekeepers
Historically, you optimized for two main gatekeepers:
- Search rankings (Google, YouTube, app stores)
- Social feeds (Meta, TikTok, X, etc.)
Now, there’s a third, more opaque layer:
- AI intermediaries that summarize, recommend, and sometimes transact on your behalf
That includes:
- Google AI Overviews and “AI Mode” experiences
- ChatGPT / OpenAI surfaces with paid placements
- AI-powered “recommendations” in search, social, email, and CRM
- Semantic search systems inside platforms (Threads, Bluesky, Discord, communities, your own site)
These systems:
- Rewrite your titles and snippets (see: 8,000 title tag rewrites case studies)
- Summarize your content instead of sending traffic
- Change recommendations on nearly every query
- Reward or ignore you based on “trust” signals you don’t fully see
The practical question is not “What is AI doing?” but:
How do we plan, buy, and measure when AI layers sit between demand and our properties?
The three invisible leaks in your current plan
1. AI cannibalization of search traffic
Ahrefs, Moz, and others are already documenting it: AI Overviews and semantic search are
answering queries that used to send you clicks.
The traffic doesn’t show up as “lost” in your analytics. It just never arrives.
Signals this is happening to you:
- Stable or rising impressions in Search Console, but flat or declining clicks
- Brand and high-intent queries still ranking, but lower CTR without clear SERP changes
- More “zero-click” behavior in your category (seen in third-party tools, not your own data)
If you’re still reporting “SEO traffic is stable” without overlaying AI surfaces,
you’re underestimating your risk.
2. AI as the new creative director of your brand
AI layers are already:
- Rewriting your titles and descriptions in SERPs
- Summarizing your content in AI Overviews
- Generating images, copy, and even product descriptions via tools your teams use
- Making “recommend or reject” decisions based on whether AI “trusts” you
This is not just a production efficiency story.
It’s a brand control story.
If you outsource too much, too blindly, you dilute the very signals these systems use to decide if you’re credible.
3. Measurement blind spots in AI-first journeys
Google Analytics is being pitched as a “growth engine.”
That’s code for: “We’ll help you optimize inside our ecosystem.”
Meanwhile, AI Overviews and other AI surfaces don’t expose full click or impression data.
You’re flying with:
- Partial attribution (AI answers that never click through)
- Lagging indicators (conversion shifts without visible top-of-funnel changes)
- Platform-defined success metrics that don’t match your P&L
The risk: you over-invest in channels where AI is quietly eating your organic reach,
then “compensate” with more paid spend in the same ecosystem.
The operating shift: from “ranking” to “being referenced”
In a semantic, AI-mediated world, the unit of competition is shifting:
- From “ranking for keywords”
- To “being referenced and recommended by machines and humans”
That sounds abstract, so let’s make it concrete.
There are four levers you can actually pull.
Lever 1: Design for semantic and AI visibility, not just SEO
Semantic search and AI Overviews don’t care about exact-match keywords.
They care about meaning, entities, and relationships.
Practical moves for your team:
-
Entity-first content planning
Map your category’s core entities: products, problems, use cases, audiences, competitors, adjacent tools.
Build content that clearly defines and relates these, not just chases volume keywords. -
Structured data as a default, not a nice-to-have
Schema markup, product feeds, FAQs, how-tos – all of it feeds AI systems.
Make structured data a non-negotiable part of every new template and content type. -
Canonical clarity
Moz’s “cannibalization” problem gets worse in AI.
When you have five similar pages, AI systems have no idea which is authoritative.
Clean up duplicates, consolidate thin variants, and make your best page unmistakably “the one.” -
Answer-level optimization
AI Overviews pull specific, concise answers.
Make sure your pages have tight, scannable answer blocks (50-200 words) for core questions,
supported by depth below.
Lever 2: Build “AI trust” as a measurable asset
Social platforms are already asking: “Does AI trust you?”
That’s not mystical. It’s pattern recognition:
- Are you cited by other credible sources?
- Do humans engage, share, and dwell on your content?
- Are your claims consistent across channels?
- Do you look like a real, operating business, not a content farm?
As a CMO or growth lead, you can treat “AI trust” as a portfolio of signals to invest in:
-
Author and brand identity
Real authors with consistent bios, social presence, and off-site mentions.
Real company details, leadership pages, and third-party coverage.
These are now ranking factors for both humans and machines. -
Reference velocity
Track how often you’re mentioned and cited in:- Industry blogs and newsletters
- Community platforms (Discord, Slack groups, forums)
- Social search surfaces (Threads, Bluesky, LinkedIn)
Don’t just count backlinks. Count references where your brand is “the example” for a topic.
-
Consistency across AI-touching surfaces
Product data in feeds, on-site, in emails, and in marketplaces should match.
Inconsistent pricing, specs, or claims are red flags for recommendation systems.
Lever 3: Treat AI intermediaries as channels in your media mix
OpenAI selling $200k+ ad commitments is the loud version of what’s happening quietly everywhere:
AI intermediaries are becoming media channels with their own economics.
You should be explicitly planning for:
-
Paid presence in AI environments
ChatGPT ads, AI-enhanced search ads with third-party endorsements,
recommendation units in marketplaces and social platforms.
These aren’t experiments anymore; they’re line items. -
Defensive spend where organic is being squeezed
If AI Overviews are eating your organic clicks for high-intent queries,
you either:- Fight to be the cited source in the Overview, or
- Shift budget into paid units that still capture that demand
This should be a conscious trade-off, not a silent default.
-
Creative tuned for AI summarization
Your ad copy and landing pages will be summarized and paraphrased by AI systems.
Write with that in mind:- Clear, concrete claims
- Distinctive positioning that survives paraphrase
- Evidence and proof points that can be lifted as “reasons” to choose you
Lever 4: Fix your measurement so AI doesn’t quietly tax your growth
You won’t get perfect visibility into AI surfaces, but you can get directional clarity.
That’s enough to make better budget calls.
Build a lightweight AI impact measurement stack:
-
AI Overview and citation tracking
Use third-party tools and manual sampling to:- Track which queries show AI Overviews in your category
- See when and how your brand is cited (or not)
- Monitor changes in wording, claims, and competitors mentioned
-
Zero-click and “view-through answer” modeling
When you see:- Stable or rising impressions
- Falling CTR
- Stable or rising brand search and direct traffic
You’re likely seeing answer-level satisfaction.
Build simple models that estimate “assisted conversions” from AI answers,
similar to how you treat view-through in display. -
Channel health reviews with AI in the room
Quarterly channel reviews should explicitly ask:- Where are AI surfaces appearing in this journey now?
- What traffic or conversions might they be absorbing?
- What experiments can we run to influence those surfaces?
If your media and analytics teams can’t answer these, that’s your hiring or training brief.
What to actually do in the next 90 days
If you’re responsible for budget and pipeline, here’s a concrete 90-day plan.
Step 1: Run an AI exposure audit
- List your top 50-100 revenue-driving queries and journeys.
- Check which now show AI Overviews, rich recommendations, or AI summaries.
- Capture where and how your brand appears – or doesn’t.
Step 2: Prioritize 10-20 “AI battleground” queries
- High intent, high revenue, clear AI presence.
- For each, define a simple goal: be cited, be the recommended tool, own the comparison, etc.
Step 3: Ship one semantic content and structure pass
- Consolidate cannibalized pages targeting the same intent.
- Add clear answer blocks, FAQs, and structured data to your top pages.
- Clean up author bios and brand credibility signals on those pages.
Step 4: Add AI surfaces to your media and reporting templates
- In your paid search and social plans, explicitly note where AI units appear.
- In your reporting decks, add a section for “AI-mediated demand” with directional metrics.
- Set a test budget for at least one AI-native paid placement (e.g., ChatGPT ads, AI-enhanced search ads).
Step 5: Decide your AI production rules
- Define where AI is allowed (e.g., first drafts, image variations, testing) and where it isn’t (final messaging, sensitive claims).
- Set review standards so your brand voice and proof survive AI paraphrasing.
- Train your teams on prompts that produce usable, not generic, outputs.
The uncomfortable but useful mindset shift
The old game was: “How do we get humans to click from platforms to our properties?”
The new game is closer to:
“How do we become the default answer machines and humans see, across surfaces we don’t own?”
That means:
- Designing for semantic understanding, not just keywords
- Investing in signals that make AI systems comfortable recommending you
- Treating AI intermediaries as real channels with budgets, targets, and tests
- Upgrading measurement so you can see, at least directionally, where AI is taxing your growth
You don’t need a grand AI “transformation” project.
You need to treat AI layers the way great operators treat every new gatekeeper:
map it, measure it, and then buy, build, or outsmart your way through it.