The quiet shift: AI is now your biggest “media partner” (whether you like it or not)
Look at those headlines and a pattern jumps out: FAQ schema, title tags, AI Overviews, ChatGPT SEO tools, Copilot usage, misinformation experiments, AI-generated career damage, Amazon squeezing partners.
Underneath all of it is one uncomfortable reality for performance marketers:
Your brand, offers, and even your reputation are now mediated by AI systems you don’t control, don’t fully understand, and can’t reliably predict.
Search results, “AI Overviews,” chat assistants, Copilot, custom GPTs-these are fast becoming the new surfaces where customers discover, compare, and decide. They’re also new surfaces where:
- Your pricing can be misquoted.
- Your competitors can be framed as “better alternatives.”
- Your brand can be associated with false or outdated claims.
- Your tracking and attribution get fuzzier as journeys move off your owned properties.
This isn’t an SEO-only problem. It’s a media buying and growth problem. If you’re responsible for pipeline, CAC, or ROAS, you now have to treat AI surfaces as:
- A new performance channel (with no clean ad unit yet).
- A new brand risk vector.
- A new attribution blind spot.
AI is quietly rewriting the funnel
The old mental model:
- Discovery: social + search + ads.
- Consideration: site, content, comparison pages, retargeting.
- Decision: pricing page, sales call, checkout.
The emerging model:
- Discovery: “What’s the best tool for X?” asked to ChatGPT, Gemini, Copilot, Perplexity.
- Consideration: AI-generated comparison summaries that may or may not reflect reality.
- Decision: Click-outs from AI answers to 1-2 “preferred” sources, often not yours.
That Ahrefs study on “Top Brand Visibility Factors in ChatGPT, AI Mode, and AI Overviews” is the canary in the coal mine: AI systems are already ranking and routing demand before users ever see a SERP or an ad.
Add in:
- Google’s Gemini upgrade powering Search and AI Overviews.
- Microsoft Copilot usage that changes by device (desktop vs mobile vs in-product).
- Custom GPTs and AI workflows that marketers themselves are building on top of these systems.
You now have an “AI layer” sitting between your media spend and your conversions. It’s influencing:
- Which brands are even considered.
- What “facts” are assumed true about you.
- Where traffic is directed when people are ready to click.
The misinformation problem is not abstract-it’s commercial
Ahrefs ran an AI misinformation experiment. A doctor had their reputation damaged by Google’s AI Overview hallucinating claims. These aren’t philosophical debates; they’re revenue and career hits.
For performance marketers, misinformation in AI systems can directly impact:
- Conversion rate: AI says you don’t integrate with a key tool → prospects bounce.
- Pricing power: AI lists your “starting price” as an old promo → you look expensive or cheap in the wrong way.
- Channel mix: AI pushes users to aggregators or affiliates → your direct acquisition shrinks, partner costs rise.
- Brand safety: AI associates you with a scandal, lawsuit, or outdated controversy.
You already fight misinformation on review sites, forums, and social. The difference now:
AI systems confidently synthesize and amplify errors at scale, in a tone users instinctively trust.
Why this matters specifically to performance and media teams
A few practical reasons this isn’t just “SEO’s problem”:
1. Attribution is about to get weirder
If users ask Copilot or ChatGPT for “best X tool,” read a synthesized answer, then click a single link, your analytics will show:
- Direct traffic.
- Organic brand search.
- Occasional referral from some AI domain.
Your media and content spend may be driving awareness that AI then captures and routes. But your models will credit the last visible touch. That distorts:
- Channel ROI decisions.
- Budget allocation between brand vs performance.
- Bids on branded vs non-branded terms.
2. Brand vs performance lines blur even more
AI systems don’t show your ad creative. They show your reputation and your structured information.
The things that influence whether AI recommends you:
- Consistency of your messaging across the web.
- Clean, structured data (schema, FAQs, product specs).
- Volume and quality of third-party mentions and reviews.
- How often you’re cited as a source in content.
That’s “brand” work with direct performance consequences. If AI rarely mentions you, your CAC goes up because you’re fighting from behind in every paid channel.
3. Platform power is consolidating-again
Amazon built a $60B ad business and is now squeezing agencies and adtech partners. Google is stuffing AI into Search. Microsoft is pushing Copilot into everything. OpenAI is turning ChatGPT into a default interface.
The pattern: platforms pull value up the stack, then compress intermediaries. That’s going to happen to:
- Comparison sites.
- Review aggregators.
- Even some affiliate models.
If your acquisition strategy relies heavily on those intermediaries, AI surfaces will gradually disintermediate them-and you-unless you adapt.
What operators should actually do about it
You can’t “fix AI,” but you can treat it like any other powerful, opaque channel: model it, monitor it, and influence it where possible.
1. Audit your “AI presence” like you audit SERPs
Once a quarter (minimum), run a structured audit across major AI systems:
- ChatGPT (with browsing / GPT-4 or latest model).
- Google Search with AI Overviews enabled.
- Microsoft Copilot (web + in-product if relevant).
- Any vertical AI tools your buyers use (e.g., Notion AI, Perplexity, industry-specific assistants).
For each, ask:
- “Best [your category] tools for [your ICP].”
- “[Your brand] pricing.”
- “[Your brand] vs [top competitor].”
- “Does [your brand] integrate with [key tools]?”
- “Is [your brand] legit / safe / trustworthy?”
Capture:
- Which brands are mentioned and in what order.
- What claims are made about you (features, pricing, positioning).
- Which URLs are cited as sources.
This becomes your “AI visibility baseline.” Treat it like rank tracking for a new, fuzzy SERP.
2. Fix your structured data and canonical signals
AI systems are hungry for:
- Clear, structured information.
- High-confidence, consistent facts across the web.
Work with your SEO and dev teams to:
- Implement and maintain accurate schema (FAQ, Product, Organization, HowTo, etc.).
- Publish a canonical “facts” page: pricing logic, integrations, industries, key features.
- Keep FAQs current and specific (not fluffy content marketing).
- Ensure your brand name, tagline, and positioning are consistent across site, socials, and major directories.
The goal: when AI systems crawl, they find one clear, up-to-date source of truth, not a mess of conflicting claims.
3. Engineer demand-side signals, not just supply-side content
AI systems don’t just read your site; they read the whole ecosystem. So:
- Get your product into real comparison content that AI might cite (not just sponsored listicles).
- Encourage customers to write detailed reviews that mention use cases, integrations, and outcomes.
- Pitch expert commentary so your team is quoted as a source in your category.
- Standardize how your brand and product names are written in partner and marketplace listings.
Think of it as “off-site prompt engineering.” You’re shaping the corpus AI trains and infers from.
4. Build AI surfaces into your testing and tracking plan
You won’t get perfect attribution, but you can get directional signal:
- Add “How did you hear about us?” fields that explicitly include “AI assistant / ChatGPT / Copilot / Gemini.”
- Tag campaigns or offers you know are heavily discussed in content AI might read (e.g., a big comparison study).
- Watch for lifts in direct and branded search after major AI product updates or PR spikes.
Over time, you’ll see whether your AI visibility work correlates with lower CAC or higher assisted conversions.
5. Create an escalation path for AI misinformation
Don’t wait for a career-damaging hallucination to hit your CEO’s inbox.
Put a simple playbook in place:
- Define what counts as “materially harmful” misinformation (legal risk, safety, pricing, security).
- Assign a cross-functional owner (usually marketing + legal + comms).
- Document contact and reporting paths for major platforms (Google, Microsoft, OpenAI, etc.).
- Prepare pre-approved language for outreach and public clarification if needed.
This is risk management, not panic. You hope to never use it-but when you need it, you’ll need it fast.
6. Treat AI as a channel in your planning, not a curiosity
In your next quarterly planning cycle, explicitly add “AI surfaces” as a line item:
- Objective: Increase inclusion and accuracy in AI answers for core category and brand queries.
- Inputs: Structured data, content updates, PR, review generation, partner alignment.
- Metrics: AI visibility audit scores, share of mentions vs competitors, correlation with branded search and direct conversions.
You’re not buying media there (yet), but you are competing for attention and trust there. Treat it with the same seriousness as a new ad network that already has half your audience.
The uncomfortable but useful mindset shift
For the next few years, assume:
- Every major platform will add an AI layer between your spend and your site.
- That layer will be imperfect, opinionated, and occasionally wrong about you.
- Users will still trust it more than you’d like.
Your job as a performance operator isn’t to worship or fight the AI layer. It’s to:
- Understand how it shapes demand in your category.
- Influence the inputs it sees and the signals it uses.
- Adjust your channel strategy and attribution models accordingly.
The marketers who treat AI surfaces as a real, messy, high-impact channel will quietly out-perform the ones still arguing about whether “AI content” is good or bad. The AI layer is here. The only question is whether it’s working for you or against you.