The shift nobody is naming: you’re marketing to ranking systems, not people
Scan those headlines and there’s a quiet pattern: AI overviews, semantic search, human experience optimization, ads in ChatGPT, “does AI trust you,” predictive tech for creators, AI-powered SEO tools, AI CRMs.
Underneath all of it is one structural shift: distribution is being taken over by opaque recommendation engines that decide what humans see – and those engines are increasingly AI-native.
That’s bigger than “SEO is changing” or “AI is the new creative intern.” It’s a channel model change. You’re no longer just optimizing for users inside platforms; you’re optimizing for the systems that decide whether users ever see you.
If you’re a CMO, performance marketer, or media buyer, this is the job now:
designing campaigns, content, and data so that recommendation engines consistently pick you – across search, social, marketplaces, inboxes, and AI assistants.
From search engines to “choice engines”
Historically, you optimized for:
- Search engines (Google, YouTube) via keywords, links, and technical SEO
- Social feeds via engagement hacks and posting cadence
- Ad auctions via bids, audiences, and creative testing
Now, the same underlying pattern is spreading everywhere:
- AI Overviews and semantic search decide if your site is cited at all, and how much traffic they siphon.
- ChatGPT, Gemini, and Perplexity are starting to run ads and affiliate-style recommendations inside conversational answers.
- Social search (TikTok, Bluesky, LinkedIn) ranks content on “helpfulness,” watch time, and creator trust, not just recency.
- Marketplaces and retail media (Amazon, Walmart, Instacart) use AI to rank products and ads by predicted conversion and customer experience.
- CRMs and email platforms quietly use AI to decide what gets delivered, prioritized, or spam-foldered.
These are all the same class of thing: choice engines. Systems that decide:
- Which content or product appears
- In what position
- For which user, in which context
The old playbook treated each channel as its own weird religion. The new reality is simpler and harsher:
you’re either consistently chosen by these engines, or you’re invisible.
What these engines are actually optimizing for
Different platforms, same math. They optimize for a small set of outcomes:
- User satisfaction (did the person feel helped, entertained, or informed?)
- Engagement depth (dwell time, scroll depth, watch time, replies, saves)
- Reliability and trust (is this source safe to show again?)
- Revenue (ad clicks, purchases, subscriptions)
That’s why you’re seeing:
- “Human experience optimization” as a ranking factor
- Guides on semantic search instead of keyword stuffing
- “Does AI trust you?” as an actual content topic
- Predictive tech for vetting creators instead of just follower counts
- AI CRMs sold on “next best action” and “propensity to buy”
The engines don’t care about your funnel, your brand pyramid, or your QBR deck. They care about how people behave after they surface you.
The uncomfortable implication: your metrics are misaligned
Most teams still optimize for:
- Click-through rate on ads
- Rank position on a keyword
- Open rate on email
- View count on video
But the engines optimize for:
- Long-session satisfaction (did the user keep engaging?)
- Task completion (did they get what they came for?)
- Complaint and bounce signals (did they back out fast, mute, block, unsubscribe?)
- Downstream value (did this user stick around and monetize?)
When you chase surface metrics, you often send the wrong signals:
- Clickbaity titles that spike CTR but tank dwell time
- Over-segmented email blasts that drive opens but also unsubscribes and spam complaints
- Over-optimized PPC landing pages that convert a small slice but repel everyone else
- AI-spun content that ranks briefly, then collapses as engagement and citations dry up
The engines notice. And once they classify you as low-value or low-trust, you pay a tax in every future auction and ranking.
Designing for recommendation engines: a practical operating system
You don’t need a new buzzword. You need a simple, shared way of working across SEO, paid, CRM, and content that assumes:
“We’re marketing to humans through AI-driven choice engines.”
1. Build an “AI trust profile” for your brand
Think of this as your credit score with algorithms. It’s made of:
- Consistency of identity: same brand name, entity data, and profiles across web, social, maps, marketplaces.
- Evidence of expertise: authors with real credentials, clear “about” pages, cited sources, case studies.
- Behavioral proof: low bounce rates, repeat visits, saved/forwarded content, high “helpful” or rating signals.
- Low-risk signals: minimal spam complaints, few policy violations, no sketchy redirects or cloaking.
Actionable moves:
- Standardize your entity data (brand, locations, people) in a central schema and push it everywhere.
- Attach real humans to content and ads; build their presence on LinkedIn, X, podcasts, and events.
- Audit spam, complaint, and unsubscribe rates as seriously as you audit CAC.
- Stop shipping content that no human would bookmark, cite, or share in a Slack channel.
2. Optimize for “post-click health,” not just acquisition
Every choice engine is watching what happens after the click, view, or open. Use that to your advantage.
Core behaviors to design for:
- Dwell time and depth: Is there a clear path to a second and third useful interaction?
- Constructive interaction: Comments, replies, saves, shares, wishlist adds, not just likes.
- Low regret: Few bounces, backtracks, unsubscribes, or “not interested” taps.
Practical plays:
- For search: design pages to satisfy the query and offer one obvious, high-value next step.
- For social: prioritize content that triggers replies and saves, not just passive views.
- For email: shrink send volume to the people who actually engage; protect your sender reputation like a P&L line.
- For paid: stop over-optimizing for cheap clicks that never convert; train algorithms on profitable post-click behavior.
3. Treat AI overviews and assistants as “zero-click channels”
AI overviews and chat answers will often resolve the user’s need without sending you traffic. That doesn’t mean they’re useless.
You can still win in three ways:
- Being cited as the source (brand visibility and authority, even if not every impression clicks).
- Being recommended when the user asks “what should I buy / use / try?”
- Feeding the models with content that shapes their understanding of your category.
How to play this:
- Create content that cleanly answers specific, high-intent questions in one place, with clear structure.
- Use schema markup and clear headings so AI systems can easily extract and quote you.
- Publish opinionated, data-backed takes that are worth summarizing, not just rehashes of generic advice.
- Track AI overview mentions and citations, not just traffic, as a leading indicator of authority.
4. Align creative and media with “behavior-first” targeting
As “behavior matters more than targeting” in AI ad products, your job shifts from slicing audiences to training the algorithm with the right signals.
That means:
- Clear, stable conversion events that actually map to value (not vanity micro-conversions).
- Enough volume on those events for the system to learn (consolidate campaigns and SKUs where possible).
- Creative that attracts the people most likely to behave well post-click, not just the most people.
Concrete moves:
- On Meta/Google: simplify structures, reduce audience fragmentation, and standardize conversion definitions across markets.
- On TikTok/YouTube: build creative templates that hook the right viewer and repel the wrong one fast.
- On retail media: feed clean product data, reviews, and availability so the algorithm isn’t guessing.
- On creator campaigns: vet creators on audience behavior (retention, comments, saves) not just reach.
5. Make “experience signals” a shared KPI across teams
If the engines are optimizing for experience, your org needs to as well. Not as a slogan – as a measurable, owned metric.
Useful shared KPIs:
- Session quality: scroll depth, time on site, pages per session, repeat visits.
- Engagement quality: comment rate, reply depth, saves, shares per impression.
- Trust health: unsubscribe rate, spam rate, complaint rate, refund rate.
- Recommendation share: share of impressions coming from “recommended,” “for you,” “suggested,” or AI overviews versus direct search.
Then:
- Give SEO, paid, CRM, and content teams joint targets on these metrics.
- In QBRs, review how changes in these signals correlate with shifts in rankings and CPMs.
- Kill tactics that hit short-term goals while degrading these signals. They’re a tax on all future performance.
How to reorganize your roadmap for the next 12-18 months
If you accept that you’re marketing to recommendation engines, your roadmap changes. Here’s a pragmatic reframe.
What to dial down
- Endless keyword-by-keyword SEO battles without looking at semantic coverage and entity strength.
- Channel-siloed optimization where email, paid, and SEO teams never compare experience metrics.
- Volume-based content calendars that flood the web (and AI models) with low-signal assets.
- Over-targeted media plans that starve algorithms of learning signals.
What to dial up
- Entity and trust work: structured data, consistent profiles, visible experts, and credible citations.
- Experience diagnostics: deep analysis of dwell time, scroll depth, and engagement quality by channel.
- AI-aware content: built to be excerpted, summarized, and recommended by assistants and overviews.
- Behavioral training: campaign structures and creative that teach algorithms who your best customers really are.
- Cross-functional governance: one owner for “algorithmic health” across organic, paid, and lifecycle.
The operators who win the next cycle won’t be the ones with the most AI tools. They’ll be the ones who understand a simple thing:
you’re in the business of shaping the behavior that trains the systems that decide your reach.