AI Is Now a Traffic Source. Treat It Like One.

The real shift: AI isn’t just a tool, it’s a channel

Most teams are still treating AI as a productivity toy or a copy machine. Meanwhile, the market has quietly moved on: AI is now a traffic source and a discovery layer that sits between you and your customer.

Look at the headlines you’ve been skimming:

• Google AI Mode and AI Overviews changing how search results appear.
• ChatGPT being searched more than YouTube, Instagram, Facebook, TikTok.
• Agentic AI and “vibe coding” as the next wave of PPC automation.
• AI voice agents and AI CRMs as front-line interfaces with customers.

Translation: discovery, evaluation, and even purchase are increasingly mediated by AI systems that are not neutral. They rank, compress, remix, and sometimes fully answer the user’s intent without sending them to you at all.

If you’re a CMO, growth lead, or media buyer, you don’t just have “search,” “social,” and “email” anymore. You have a new category:

AI surfaces as a channel.

Treat it that way or you’ll spend the next two years wondering why your branded search looks fine while your pipeline quietly erodes.

The AI surface area: where you’re already being filtered

AI as a channel shows up in four main places today:

1. AI-augmented search (Google AI Mode, AI Overviews, Bing Copilot)

These systems:

• Summarize multiple sources into one answer.
• Pull in a small set of “cited” pages.
• Heavily factor user data and intent history (per Liz Reid’s DOJ filing and ongoing coverage).

That means your classic blue-link SEO play is now competing with:

• Being cited in AI Overviews.
• Being used as a training/reference source behind the scenes.
• Being completely abstracted away so the user never sees you.

2. AI-native discovery (ChatGPT, Claude, Perplexity, etc.)

Users are now asking models:

• “Best B2B CRM for a 20-person sales team?”
• “How do I set up TikTok Shop for a US-based brand?”
• “What’s the best email cadence for ecommerce?”

That’s category discovery and vendor shortlisting happening entirely inside an AI interface. No SERP. No feed. No pre-roll. If you’re not part of the model’s “mental map” of your category, you’re invisible.

3. AI-managed media (agentic PPC, “vibe coded” campaigns)

The PPC headlines are all pointing the same way: Google and others want you to hand over:

• Your budget.
• Your assets.
• Your goals.

And then let their systems decide:

• Where to show you (Search, YouTube, Discover, Demand Gen, podcasts, partner inventory).
• What to say (auto-generated creatives, dynamic text, AI headlines).
• Who to hit (audience signals plus their black-box models).

That’s not a bid manager. That’s an agent. You’re no longer directly buying placements; you’re negotiating with a system that optimizes for its own engagement and revenue constraints as well as your goals.

4. AI as the front line (voice agents, AI CRM, support bots)

AI voice agents, AI CRMs, and support bots are now the first (and sometimes only) “touch” for a customer. They:

• Shape perception of your brand’s competence and tone.
• Decide which offers, content, or upsells to present.
• Generate the actual words customers read or hear.

This is no longer “operations tooling.” It’s a live, always-on media channel that you barely QA.

The operational problem: you’re flying blind on a new channel

Most teams have:

• No owner for “AI surfaces.”
• No KPIs tied specifically to AI-mediated discovery.
• No instrumentation for how AI systems are representing their brand.

At the same time:

• SEO teams are fighting cannibalization and title tag rewrites.
• Media teams are told to trust black-box AI bidding strategies.
• Copy and brand teams are under pressure to “just use AI” while also preserving “taste” and trust.

The result is what ID Comms called out between agencies and clients: a fog around data and decision-making. Everyone feels performance shifting, but no one can point to a clean dashboard and say: “Here’s what AI is doing to us this quarter.”

A practical way forward: treat AI like search in 2005, not like magic in 2026

The teams who win this phase won’t be the ones with the flashiest AI stack. They’ll be the ones who treat AI as a channel early, with boring, rigorous habits.

1. Create an “AI surfaces” owner and scoreboard

First, give this a home. If everything is “everyone’s job,” it’s nobody’s job.

Depending on your org, the owner could sit in:

• Growth (if you’re performance-led).
• Product marketing (if you’re category-led).
• SEO/content (if search is your main acquisition engine).

Their mandate:

• Map where AI is mediating your customer journey today.
• Set a small, brutal set of KPIs for that surface.
• Coordinate SEO, paid, content, and CRM to influence those KPIs.

Example KPIs:

• Share of AI Overview citations on top 50 category queries.
• Presence in AI-native answers for “best X tools” queries.
• AI-driven traffic and assisted conversions (from tools that expose referrers, or via controlled tests).
• Resolution rate and NPS/CSAT for AI-powered support and sales flows.

2. Optimize for AI Overviews and AI answers like you once did for blue links

The Ahrefs data on ranking in AI Overviews is clear on one thing: speculation is useless; patterns matter. Across multiple studies and case work, a few practical signals keep showing up:

• Clear, structured answers to specific questions (H2/H3s that match real queries, concise answer blocks, then depth).
• Topical depth rather than thin, scattered posts. Fewer, better pages that fully cover a topic beat dozens of near-duplicates.
• Low cannibalization. If you have eight half-overlapping pages on the same intent, you confuse both classic search and AI summarizers.
• Evidence and specificity. Data, examples, and concrete numbers tend to get cited more than vague how-tos.

Operational moves:

• Run a cannibalization audit on your top 100 queries. Merge, redirect, or clearly differentiate pages that target the same intent.
• Rewrite key pages with a “teach the model” mindset: answer the question in 2-3 crisp sentences, then expand.
• Mark up content with schema where relevant (FAQ, HowTo, Product, Organization) to give models cleaner structure.
• Monitor which pages are being cited in AI Overviews and double down on their quality and internal linking.

3. Make your brand “model legible”

Large models build an internal map of:

• Who you are.
• What you sell.
• Where you fit in the category.

Your job is to make that map obvious.

Practical ways to do that:

• Consistent category language. Don’t call yourself a “revenue orchestration layer for modern teams” in one place and a “CRM” in another. Pick a plain-English category and use it everywhere.
• High-authority mentions. PR and thought leadership aren’t just for humans anymore. When reputable sites describe you clearly, models pick that up.
• Structured data about your company (Organization schema, product schema, pricing pages that are not buried in PDFs).
• Own your founder and brand narrative. When your leadership shows up in interviews, podcasts, and bylines with consistent messaging, models have a clearer representation of your positioning.

Ask a simple question in multiple models: “Who are the main players in [your category]?” If you’re not mentioned, that’s your early warning signal.

4. Treat AI media buying as a negotiation, not a surrender

Agentic PPC and “vibe-coded” campaigns sound fun until you realize you’ve given a black box full control of:

• Your bids.
• Your placements.
• Your creative mix.

You can’t fully reverse this trend, but you can bound it.

Practical guardrails:

• Separate “explore” and “exploit” budgets. Keep a fixed percentage of spend in tightly controlled campaigns with transparent keywords, audiences, and placements. Use the rest in AI-driven formats, but compare outcomes.
• Control the inputs ruthlessly. Don’t dump your entire creative library into Performance Max or Demand Gen. Curate assets that match your actual positioning and margins.
• Instrument incrementality. Run geo splits, holdouts, or time-based tests to see what AI-managed campaigns are truly adding versus cannibalizing from branded or organic.
• Audit creative outputs regularly. Spot-check AI-generated ads for claims, tone, and alignment with your brand and regulatory constraints.

5. Design AI touchpoints with “taste” and trust, not just throughput

There’s a quiet backlash brewing around AI-generated content: it often feels generic, and users are starting to notice. Social Media Examiner’s “Does AI Trust You” framing is the right inversion of the usual question.

You shouldn’t just ask, “Can we trust AI?” You should ask, “Can AI trust us enough to recommend us?” and “Can users trust that what they’re reading or hearing from us was crafted with care?”

Operationally:

• Define non-negotiables for AI-generated messaging. For example: no fabricated numbers, no competitor bashing, no health claims, no invented testimonials.
• Use AI for scaffolding, not final copy, in high-stakes surfaces. Landing pages, sales emails, and product descriptions still need human editing for taste and specificity.
• Give your AI agents a style guide. Tone, vocabulary, what you never say, and how you handle uncertainty (“I’m not sure” is better than hallucination).
• Measure human outcomes, not AI throughput. Faster ticket resolution is meaningless if NPS drops because the bot feels like a wall.

What to do this quarter

If you want a clear starting point that doesn’t require a re-org or a seven-figure AI budget, here’s a 90-day plan:

Month 1: Map and measure

• Assign an “AI surfaces” owner.
• Run a quick audit: where do AI systems touch your funnel today? (Search, chatbots, CRM, support, media buying.)
• Ask major models 10-20 key questions about your category and screenshot the answers. Note if and how you appear.
• Set 3-5 KPIs specific to AI surfaces (citations, presence, AI-assisted conversions, bot resolution rates).

Month 2: Fix the obvious structural issues

• Clean up content cannibalization on your top topics.
• Rewrite or consolidate a handful of core pages with clear, structured answers.
• Implement basic schema markup where missing.
• Define guardrails for AI-generated messaging and update your style guide.

Month 3: Experiment and instrument

• Run a small controlled test with an AI-native discovery channel (e.g., sponsored answers where available, or content tailored for AI-first discovery platforms).
• Split your media budget into controlled and AI-managed segments with clear comparison metrics.
• Instrument your AI support or sales flows with satisfaction scores and escalation tracking.
• Re-run your model queries from Month 1 and see if your presence or framing has shifted.

The teams who treat AI as a channel now will treat the next two years like an arbitrage window, not a mystery. Everyone else will be stuck arguing about “brand versus performance” while an entirely new layer of distribution quietly decides who even gets to play.