The real shift: AI is no longer a tool, it’s a distribution layer
Look at those headlines as a single story and a pattern jumps out:
- Google AI Mode, AI Overviews, “user data is important in Google Search”
- ChatGPT gets more searches than YouTube, Instagram, Facebook, TikTok
- AI bots being blocked, domains still matter, CRM and AI, AI voice agents
Everyone is talking about “AI in the funnel” and “AI in the stack”. That’s fine. But the thing that actually matters to CMOs and performance operators right now:
AI is becoming its own traffic channel and discovery surface, with its own rules, its own ranking factors, and its own failure modes.
If you’re still treating AI as a productivity feature instead of a distribution channel, you’re going to lose share in search, social, and CRM at the same time – and not understand why your CAC quietly climbs.
From search engine to answer engine to agent
Three shifts are happening in parallel:
-
Search is becoming AI-first.
Google AI Mode and AI Overviews sit on top of traditional rankings. They compress multiple results into one “answer block”. That’s a new SERP position above position #1. -
AI assistants are becoming the interface.
ChatGPT is now searched more than most social platforms. Copilot, Perplexity, Gemini, Claude – they’re all front doors to information and products, not just toys. -
AI agents are starting to transact.
AI voice agents, AI CRM, TikTok Shop changes, and “AI shopping can’t just scrape information” all point to the same future: assistants making or heavily steering purchase decisions.
That means you’re no longer just optimizing for:
- Human readers
- Search engine crawlers
- Social algorithms
You’re optimizing for AI summarizers and decision-makers that:
- Read across your site, competitors, reviews, Reddit, Substack, product feeds
- Compress it into one “best answer”
- Decide whether to mention you, ignore you, or misrepresent you
That’s a distribution problem, not a “cool AI feature” problem.
The uncomfortable question: does AI trust you?
Several of the headlines circle the same anxiety: “Recommended or Rejected: Does AI Trust You”, “AI’s trust problem”, “user data is important in Google Search”.
In practice, AI systems are doing a version of what good media buyers and SEOs already do:
- Weight sources by authority and recency
- Cross-check claims across multiple domains
- Down-rank noisy, inconsistent, or obviously promotional content
The difference is scale and indifference. An AI doesn’t care about your brand narrative. It cares about:
- How consistent your data is across the web
- How often you show up as a cited, linked, or quoted source
- Whether user behavior suggests you satisfy intent
- Whether your content is structured in a way it can easily parse
That’s why operators keep bumping into weird failures:
- AI Overviews citing a tiny competitor as “the” solution
- ChatGPT recommending an outdated pricing page
- AI summaries mis-stating your product capabilities
The core problem: your AI surface area is unmanaged.
Stop thinking “SEO + AI”. Start thinking “AI-native discoverability”.
Most teams are trying to bolt AI on to old SEO and content playbooks:
- “Let’s use AI to write more blog posts.”
- “Let’s generate 8,000 title tags.” (Someone literally did.)
- “Let’s ask ChatGPT for content ideas.”
That’s not strategy. That’s adding a motor to a horse-drawn cart.
AI-native discoverability asks a different question:
If a user asked an AI assistant for the problem you solve, what would the AI confidently say about you – and why?
That breaks down into four operator-level concerns:
- Can AI systems see your data in structured, up-to-date form?
- Do other credible sources agree with your claims?
- Does user behavior suggest you’re a safe recommendation?
- Is your brand “explainable” in one or two crisp sentences?
Practical moves: how to treat AI as a channel in your plan
Let’s make this operational. If you run growth, media, or performance, here’s how to treat AI as a first-class channel over the next 12-18 months.
1. Build an “AI-facing” source of truth
You already have brand guidelines and a messaging doc. You need the technical version of that for machines.
At minimum:
-
Structured product and pricing data
Use schema markup, product feeds, and well-documented APIs so AI systems can see:- What you sell
- Who it’s for
- Key features and constraints
- Current pricing and plans
-
Canonical “about” and “compare” pages
Create pages that:- Explain your category and where you fit
- Compare you to alternatives honestly (not just “us vs them” fluff)
- Use clear, factual language that’s easy to summarize
-
Public documentation that’s actually readable
Docs, FAQs, and help content are catnip for AI systems. If your docs are thin, gated, or outdated, you’re training the models to ignore you.
2. Treat AI Overviews and answer boxes as inventory
AI Overviews, featured snippets, and answer cards are now premium placements. They behave like a new type of ad slot or organic unit:
- High intent
- High compression (very few brands mentioned)
- High influence on click behavior
Operationally:
-
Map “AI answer” keywords
Identify terms where:- AI Overviews consistently show
- Your category is being summarized (e.g., “best X for Y”, “how to do Z with [tool type]”)
-
Audit how you’re described
Literally ask major assistants:- “What is [your brand]?”
- “Best tools for [problem you solve]?”
- “Alternatives to [your brand]?”
Document:
- Are you mentioned?
- Is the description accurate?
- Which sources are being cited?
-
Backfill the missing citations
If AI keeps citing:- Specific review sites
- Specific “best of” lists
- Specific docs or comparison pages
…and you’re absent there, that’s your to-do list.
3. Optimize for “AI taste”, not just human taste
There’s a lot of talk about “taste” in marketing. In this context, think of “AI taste” as the patterns models use to decide what sounds:
- Authoritative
- Non-spammy
- Useful and specific
Practically, content that wins with AI tends to be:
- Specific over vague (“increase inquiries by 37%” beats “grow your business”)
- Grounded in data, examples, and steps
- Clear about scope and limitations
- Less obsessed with branded adjectives, more with facts
That’s why case studies like “Tackling 8,000 title tag rewrites” or “37% more inquiries” keep getting cited. They’re structured, concrete, and easy to compress into “evidence”.
For your team:
- Write for skimmability and summarization (short sections, clear headings, explicit numbers)
- State your claims in simple, declarative sentences AI can quote
- Avoid stuffing every paragraph with brand-speak that sounds like ad copy
4. Close the feedback loop: measure AI as you would any channel
You won’t get perfect attribution from AI surfaces, but you can get directional signals.
Build a simple AI visibility scorecard:
-
Coverage
For your top 50-100 category and problem terms:- Are AI Overviews showing?
- Are you mentioned?
-
Accuracy
For “what is [brand]” queries across assistants:- Is the description correct?
- Are core differentiators present?
-
Traffic proxies
Track:- Brand search volume trends after big AI feature launches
- Direct traffic and “no referrer” sessions around the same time
- Changes in performance for pages frequently cited by AI
You don’t need perfection. You need a trend line you can tie to media and content decisions.
5. Align media buying with AI-shaped journeys
AI is changing the path to click, not just the click itself.
A realistic 2026 journey:
- User sees your ad or a creator mention on TikTok / Instagram.
- Instead of Googling, they ask ChatGPT or Copilot “is [brand] good for X?”
- The assistant summarizes reviews, your docs, and competitor pages.
- They click one of the cited sources or go direct to your site.
If your paid team is only optimizing:
- Ad CTR and CPA
- Last-click or platform-reported ROAS
…you’ll miss the hidden assist from AI and under-invest in the content and surfaces that make your ads “safe to click” in an AI-mediated world.
Practical adjustments:
- Budget for “AI-influencing” content (reviews, comparisons, docs) as part of media, not just brand
- Coordinate launches: when you push a new campaign, ensure AI-facing content is updated the same week
- Use post-purchase surveys to ask “Did you use ChatGPT / Copilot / AI search in your research?” and track the trend
The quiet risk: AI can erase you faster than it can grow you
The industry is obsessed with AI as a productivity booster. The more urgent reality: AI can quietly:
- Compress your organic reach into a single generic answer
- Route demand to aggregators and marketplaces instead of your site
- Misrepresent your pricing, features, or positioning at scale
That’s not a future risk. It’s already showing up:
- Publishers being sidelined in AI news summaries
- Brands discovering AI recommends “alternatives” that don’t actually compete
- Marketplaces and review sites being cited as the primary source of truth
The teams that win the next few years won’t be the ones who “use more AI”. They’ll be the ones who treat AI as:
- A channel to plan against
- A surface to optimize
- A critic to persuade
Put “AI distribution” on the same level as search, social, and CRM in your planning deck. Assign an owner. Build a scorecard. Treat the models like a new, ruthless media partner:
They don’t care about your story. But they do care about your data, your consistency, and whether users stick around after they send you traffic. That’s the game now.