The real shift isn’t “AI in marketing.” It’s AI eating your visibility.
Look past the hype and the headlines and there’s one pattern that actually matters to operators right now:
Search and discovery are being quietly rebuilt around AI systems that:
- Answer questions directly (Google AI Overviews, ChatGPT, Gemini, Perplexity, etc.)
- Rewrite what users see (title rewrites, summaries, snippets)
- Decide who to “trust” and recommend (semantic search, authority scoring, AI “trust” signals)
- Hide or compress your traffic (zero-click answers, opaque attribution, missing referrers)
This isn’t an SEO story. It’s a visibility and media efficiency story.
If you run a growth P&L, your future paid and organic performance is being taxed by systems you don’t yet measure, don’t yet brief against, and don’t yet buy.
From “rank on page 1” to “be the answer in the model”
The old game:
- Rank on page 1
- Win the click
- Convert on your site
The new game:
- Be selected as a source by AI systems
- Be summarized or cited in the answer
- Capture demand even when the click never happens
Headlines about AI Overviews, semantic search, custom GPTs, “Does AI trust you?”, and “What 2 million LLM sessions reveal about AI discovery” are all describing the same thing:
your brand is increasingly mediated by AI layers you don’t control.
That has three commercial consequences:
- Organic traffic numbers will look worse than your true “influence” on decisions.
- Paid search and social CPAs will drift up as AI answers siphon off cheap, high-intent clicks.
- Brand and performance will converge in the AI layer, whether your org structure is ready or not.
AI visibility is not SEO 2.0. It’s a new performance surface.
Most teams are treating this as “SEO changes.” That’s too small.
AI visibility spans:
- Search: Google AI Overviews, Bing Copilot, Gemini, Perplexity
- Social: Threads and Bluesky “SEO,” TikTok search, recommendation feeds
- Assistants: ChatGPT, custom GPTs, browser copilots, CRM copilots
- Owned interfaces: Your own AI chat, site search, product finders
In all of these, the pattern is the same:
semantic systems decide what is relevant, credible, and “on-brand” for the user’s intent.
That means:
- Keywords still matter, but as weak hints, not the main event.
- Topical depth, consistency, and clarity matter more than ever.
- Brand signals (mentions, sentiment, authority) bleed directly into performance outcomes.
Why your dashboards are lying to you
As AI layers expand, three measurement gaps open up:
1. The “invisible influence” gap
AI Overviews, LLM answers, and social recommendations can:
- Use your content to shape an answer
- Omit a visible citation
- Send the user to a different site or keep them on-platform
Your analytics show:
- No click
- No session
- No attribution
But your content still influenced the decision. This is the organic equivalent of view-through conversions in display and CTV: annoying to measure, commercially real.
2. The “re-written intent” gap
Google rewriting 8,000 title tags isn’t just a UX tweak. It’s the platform:
- Reframing what your page is “about”
- Testing different hooks against user behavior
- Potentially misaligning with your conversion goal
If AI systems summarize your page differently than you wrote it, your:
- CTR changes
- Audience mix changes
- Downstream conversion rate changes
Most teams are not even logging when and where rewrites or AI answers appear, let alone tying them to performance shifts.
3. The “channel fiction” gap
Search Engine Land’s “Is SEO a brand channel or a performance channel? Now it’s both” is the right question.
In an AI-first environment:
- Your brand strength influences AI selection.
- Your performance content feeds brand understanding in the model.
- Your PR, social, and community presence become features in the AI’s ranking logic.
Treating SEO as a pure performance channel or pure brand channel is now a budgeting fiction. The AI layer doesn’t care which team owns what.
A practical playbook: designing for the AI layer, not just the SERP
You don’t need a 5-year “AI transformation.” You need a 12-18 month operating plan that treats AI visibility as a first-class performance surface.
1. Build an “AI visibility” dashboard
Stop waiting for perfect data. Start with directional instrumentation:
- Track AI Overviews and citations for your top 200-500 queries:
- Where do AI answers appear?
- Are you cited? How often? In what context?
- Which competitors are consistently in the answer set?
- Monitor title and snippet rewrites:
- Log when your titles differ from your HTML.
- Correlate with CTR and conversion changes.
- Watch zero-click behavior:
- Impressions up, clicks flat/down on key terms = AI or SERP features eating clicks.
- Layer this into your paid search bidding strategy.
- Track brand mentions in AI contexts:
- Ask LLMs structured questions about your category and brand.
- Log which brands they recommend and why.
This doesn’t need to be perfect. It needs to be consistent enough to inform budget and creative choices.
2. Design content for semantic and AI consumption, not just humans + keywords
AI systems do well with:
- Clear definitions
- Structured comparisons
- Step-by-step processes
- Explicit pros/cons and tradeoffs
- Concrete data and examples
That means:
- Cluster around problems, not products. Build deep topical hubs (guides, FAQs, tools, calculators) around the jobs your buyer is trying to get done.
- Use consistent language. If your product solves “invoice reconciliation,” call it that across site, docs, and PR. Semantic systems reward clarity.
- Answer the uncomfortable questions. “Is X worth it?”, “X vs Y”, “Why X fails.” If you don’t, AI will use someone else’s content to answer.
- Structure content for parsing. Clean headings, tables, bullets, and schema markup help both search engines and LLMs understand your content.
3. Treat AI trust as a brand asset
Social Media Examiner’s “Recommended or Rejected: Does AI Trust You” gets at something bigger:
AI systems are building their own view of your brand.
You can influence that by:
- Consistency across channels. Product claims, pricing, positioning, and target segments should match across site, app stores, PR, and social.
- Evidence, not adjectives. Case studies, benchmarks, and third-party reviews are more likely to be quoted and reused by AI than fluffy copy.
- Author and entity clarity. Real authors, real experts, clear company information. E-E-A-T isn’t dead; it’s just being baked into models.
- Owning your narrative in niche communities. Discords, forums, and social threads often seed the examples and opinions AI systems pick up.
4. Rebalance brand vs performance around the AI choke point
If AI is the new choke point between user intent and your site, then:
- Brand work that improves recognition, trust, and mention volume will improve your odds of being selected in AI answers.
- Performance work that improves landing pages, offers, and conversion mechanics will extract more value from the smaller share of clicks you do get.
Practically, that suggests:
- Shift a slice of “pure performance” budget into high-signal brand content:
- Category-defining guides
- Original research
- Opinionated POV pieces that others quote
- Stop funding content that only works as long-tail SEO filler. If it’s not helpful enough for an LLM to reuse, question why you’re producing it.
- Align PR, content, and paid search around a shared set of category narratives and terms.
5. Use AI as an operator, not just a subject
While everyone debates AI ethics and regulation, the practical edge right now is operational:
- Automate the grunt work:
- Use custom GPTs and scripts to crawl SERPs, log AI Overviews, and flag changes.
- Bulk-analyze title rewrites and meta vs displayed copy.
- Cluster queries by intent and AI treatment.
- Stress-test your brand in LLMs:
- Ask: “Which tools should I consider for [your category] and why?”
- Ask: “What are the downsides of [your brand]?”
- Ask: “What’s the best way to solve [core customer problem]?”
- Feed your own models:
- Train internal GPTs on your best-performing content, FAQs, and sales objections.
- Use them to standardize messaging and accelerate creative testing, not to outsource thinking.
What CMOs and performance leaders should actually do this quarter
To move this from theory to roadmap, here’s a 90-day agenda you can drop into your operating plan.
30 days: visibility and baseline
- Identify your top 200-500 queries by revenue impact (paid + organic).
- Audit which of these:
- Trigger AI Overviews or similar AI answers.
- Show rewritten titles/snippets.
- Have rising impressions but flat/down clicks.
- Run a structured LLM brand audit:
- How often are you recommended vs key competitors?
- What reasons are given?
- What misconceptions or gaps show up?
- Get one simple AI visibility dashboard live, even if it’s ugly.
60 days: content and bidding adjustments
- Prioritize 10-20 high-value queries where:
- AI answers exist and you’re absent or misrepresented.
- Clicks are dropping but revenue from the category is not.
- Ship content and page updates designed for AI consumption:
- Clear definitions, comparisons, FAQs, and explicit “who this is for / not for.”
- Schema markup and cleaner information architecture.
- Adjust paid search strategy:
- Bid more selectively on terms where AI answers dominate and your organic presence is weak.
- Protect high-intent branded and category terms where AI is starting to intermediate.
90 days: org and budget alignment
- Define “AI visibility” as a shared KPI across SEO, content, PR, and paid search:
- Share a single list of priority narratives, terms, and queries.
- Agree on how you’ll measure progress (citations, share of AI answers, assisted revenue).
- Reallocate a small but meaningful slice (5-10%) of performance budget to:
- Original research and data assets.
- Category-level content that is likely to be quoted and reused.
- Set a quarterly AI discovery review:
- What changed in AI answers, Overviews, and social search?
- Where did we gain or lose presence?
- How did that correlate with performance metrics?
The platforms are already optimizing for an AI-first discovery world. The question is whether your marketing organization is planning for the same reality, or still optimizing for a search environment that quietly stopped existing two years ago.