The real shift hiding in all these headlines
Ignore the noise about “AI strategy” for a second and look at the pattern in those headlines:
- On-page AEO (AI Engine Optimization)
- Agent runtime wars
- Google expanding AI search links without click data
- “If AI can’t find you, neither can your clients”
- Claude skills, RAG, APIs, training data
The signal: your media, content, and offers are being re-interpreted, summarized, and recommended by AI agents that never show a browser and never send a click.
We are moving from “optimize for people who search” to “optimize for agents that decide.” That breaks most of the mental models CMOs, performance marketers, and media buyers still use.
This isn’t a thought experiment. It’s already affecting:
- How Google, TikTok, and Reddit traffic behaves
- How attribution looks in your dashboards
- How your brand shows up (or doesn’t) inside AI assistants, tools, and “copilots”
The operators who adjust now will quietly gain share while everyone else keeps arguing about last-click vs MMM on a shrinking pie.
The new funnel: from search box to agent runtime
The old funnel had a clear spine:
- Person types query into Google or a social search bar
- They click through to your site or landing page
- You track the session, attribute the channel, optimize the path
The new funnel increasingly looks like this:
- Person asks an AI agent (chatbot, copilot, OS assistant, in-app helper) for a recommendation or to “just do it”
- The agent hits a mix of:
- Search engines (AI overviews, SGE, TikTok search, Reddit)
- APIs and plugins (booking, shopping, SaaS integrations)
- Its own vector database / RAG index (docs, reviews, product feeds)
- The agent returns a decision, not a SERP:
- “I’ve booked you at X hotel.”
- “Here are the three best tools, I recommend this one.”
- “I’ve drafted the email using Y template provider.”
That means:
- Fewer visible impressions
- Fewer clicks
- More off-screen decisions that still affect your revenue
You are no longer just optimizing for a human scanning a page. You are optimizing for machine intermediaries that:
- Parse your content
- Score your credibility
- Negotiate tradeoffs on behalf of the user (price vs quality vs convenience)
AEO is not “new SEO.” It’s a different game.
A lot of current content treats “AI Engine Optimization” as warmed-over SEO: write clearer content, answer questions, add schema, hope for the best.
That’s incomplete. AEO is closer to product distribution plus technical integration plus narrative control.
Think of three layers:
1. Machine readability: can agents even parse you?
This is the baseline. If your site and feeds are a mess, agents will either skip you or misrepresent you.
- Structured data everywhere: product schema, FAQs, how-tos, org markup, pricing where appropriate.
- Canonical clarity: resolve cannibalization. One definitive URL per core topic or offer. Agents hate ambiguity.
- Plain-language answers: short, direct, copyable answers to high-intent questions. Agents quote these verbatim.
- Clean information architecture: logical URL structure, consistent naming, minimal duplication.
This is not about chasing micro-optimizations. It’s about making your site feel like a clean API, not a junk drawer.
2. Agent distribution: where can AI actually “touch” your brand?
Most teams are still thinking “rank on Google.” Meanwhile:
- Agents are pulling from Reddit, niche communities, and Q&A threads
- Vertical AIs are indexing app marketplaces, plugin stores, and integration catalogs
- Enterprise copilots are trained on internal docs, partner portals, and vendor libraries
Ask three blunt questions:
- In my category, which AI surfaces actually drive decisions?
- Developer tools: GitHub Copilot, Claude, Stack Overflow-style communities
- SMB SaaS: app stores (Shopify, HubSpot, Salesforce), template galleries, CRM copilots
- Consumer: TikTok search, Reddit, Google AI overviews, shopping agents
- Do those surfaces see my brand as a first-class object or just a random URL?
- Do I have an official integration, plugin, or app?
- Are my products in structured feeds those agents ingest?
- Are my docs and help content accessible and crawlable?
- Where am I completely invisible even though my buyers are active?
AEO here means treating:
- App stores as search engines
- Integration directories as category pages
- Reddit, Discord, Slack communities as training data sources
3. Narrative signals: what do agents “believe” about you?
AI systems are pattern matchers. They pick up:
- How often your brand appears next to certain problems or use cases
- How people describe you in reviews, threads, and comparisons
- Whether you show up in “best X for Y” lists with consistent positioning
This is where positioning and category design suddenly become very operational:
- One sentence, everywhere: a consistent, concrete positioning line that appears in bios, descriptions, and intros across the web.
- Deliberate comparison content: honest, structured “X vs Y” and “Best tools for Z” content that agents can quote.
- Review hygiene: enough volume, recency, and specificity in reviews that models can form a pattern.
If you do not define your narrative in machine-readable ways, AI will assemble one for you from whatever scraps it finds. You may not like the result.
Why your media buying math is about to break
When decisions move inside agents, three things happen to your numbers:
- Click-based attribution undercounts reality.
A user asks an AI for options, sees your brand, then:
- Goes direct via branded search
- Types your URL
- Asks an internal copilot to “set up with <your brand>”
Your AI visibility drove the decision, but the last click looks like “Direct” or “Brand Search.” Your AI work looks like it’s doing nothing.
- Top-of-funnel channels become training data, not just traffic sources.
Memes, short-form video, and community content on Reddit, TikTok, and Instagram are not only driving human awareness. They are also:
- Feeding the data that AIs use to map your brand to specific problems and audiences
- Shaping the language people use when they ask agents about your category
- “Perfectly set-up but poor performing” campaigns become more common.
You can have:
- Flawless targeting and creative
- Great CTR and decent conversion
- And still lose, because when users later ask an AI what to buy, you’re not in its mental model
The tactical takeaway: you need a measurement stack that assumes untracked AI touchpoints exist and manages to signal, not just clicks.
What to actually do in the next 6-12 months
Here’s a concrete roadmap that a CMO or head of growth can push through without boiling the ocean.
1. Run an “AI visibility audit” of your brand
This is not a vanity exercise. It’s your new distribution check.
- Ask major AIs real buyer questions in your category.
- Use at least: ChatGPT, Claude, Gemini, Perplexity, plus any vertical tools in your space.
- Prompt like a buyer: “What are the best <category> tools for <segment> that do <job>?”
- Track: Do you appear? How are you described? Who dominates?
- Search your brand and key problems on Reddit, TikTok, LinkedIn, and niche communities.
- Is there any organic conversation?
- What language do people use to describe their pain and your solution?
- Check app stores and integration directories.
- Are you present where your buyers live (Shopify, HubSpot, Salesforce, Figma, Notion, etc.)?
- Is your description consistent with your positioning?
Output: a simple matrix of “Where we appear / how we’re framed / who owns the narrative if not us.”
2. Fix your machine-readable basics
Give this to your SEO and web teams as a focused project, not an endless backlog.
- Resolve obvious cannibalization on key topics and offers.
- Add or clean up schema for:
- Products / services
- FAQs and how-tos
- Organization and authors
- Reviews and ratings (where appropriate)
- Rewrite critical pages with:
- Clear, copyable definitions of what you are and who you’re for
- Direct Q&A sections that mirror real buyer questions
Treat this as infrastructure. You do it once properly, then maintain.
3. Put your brand inside the agent ecosystem
This is the part most teams skip because it feels “technical.” It’s actually distribution.
- Prioritize one or two key integrations or plugins.
- If your product is used inside another tool, build the official app or integration.
- Write the listing like a landing page: who it’s for, what job it does, why it’s different.
- Expose clean APIs or feeds where it makes sense.
- Product feeds for shopping agents
- Content feeds for research agents
- Pricing and availability where appropriate
- Make your docs and help content public by default (unless there’s a good reason not to).
4. Engineer your narrative for both humans and models
Sit down with brand, content, and performance together. Align on:
- The one-line positioning you want repeated everywhere.
- Three to five canonical use cases you want to be “the answer” for.
- Comparison angles that matter: who you’re an alternative to, and why.
Then:
- Update all bios, intros, and descriptions to match that one line.
- Create structured “Best X for Y” and “X vs Y” content that is honest, specific, and easy to quote.
- Seed those narratives into:
- Guest posts and PR
- Community threads (without astroturfing)
- Partner content and integration pages
5. Adjust your measurement and planning assumptions
You do not need a perfect AI attribution model. You do need to stop pretending every important touchpoint is a tracked click.
- Use more directional measurement: brand lift studies, post-purchase surveys that explicitly ask “Did you use an AI assistant or chatbot in your research?”
- Set “AI visibility” as a leading KPI: share of recommendations in AI tests, presence in “best tools” answers, etc.
- Budget for “unattributed but necessary” work: narrative content, integrations, community presence that you know feeds AI even if it does not show up cleanly in GA or Ads Manager.
The operators who win this phase
The winners in the AI agent era will not be the ones with the flashiest “AI-powered” marketing decks. They will be the ones who quietly:
- Make their brands easy for machines to understand
- Show up in the right agent-accessible surfaces
- Control a clear, consistent narrative that both humans and models can repeat
- Plan media and measurement around the reality that many decisions now happen off-screen
If AI agents are the new browsers, your job is simple and hard at the same time: become the default answer they reach for.