The real shift: humans aren’t your primary audience anymore
Across those headlines, one pattern jumps out: everything important in marketing right now is about intermediaries.
Not just Google’s algorithm updates, TikTok sales, or Instagram tools. The bigger shift is this:
AI systems and platforms are becoming the primary “deciders” of what your customer sees long before your customer ever does.
Search is turning into “answer engines” and agent runtimes. Feeds are more algorithmic and compressed. AI models are trained on your content, summarize your offers, and recommend alternatives. Retail and streaming platforms are pitching their ad tech, not just their audiences.
If you’re still planning media and creative as if you’re talking directly to people, you’re already behind. You’re now marketing to:
- AI search and “generative engines”
- Agent runtimes and recommendation systems
- Closed ad platforms that optimize to their own economics, not yours
That sounds abstract. It isn’t. It’s a performance problem. Your traffic, attention, and revenue are being intercepted by systems that don’t care about your brand, only about user satisfaction and platform profit.
The operators who win the next 3-5 years will treat these intermediaries as a new, very real audience: one you can study, influence, and design for.
From SEO to AEO to “agent optimization”: what’s actually changing
Look at the cluster of topics in the headlines:
- “On-Page AEO: 4 Writing Frameworks for Better AI Visibility”
- “The Agent Runtime Wars Have Begun. Is Your Website Ready?”
- “6 generative engine optimization benefits every marketer should know”
- “How Does AI Get Its Information? Training Data, RAG, MCPs, and APIs Explained”
- “AI in marketing examples and strategies you can use today”
- “AI’s trust problem: The cost of outsourcing your message in a SaaS recession”
The through-line: machines are now the first reader, first buyer, and first recommender of your marketing.
Three big shifts matter for CMOs and performance leaders:
1. Search is turning into an answer layer, not a traffic faucet
Google’s core updates, AI overviews, and “no longer supporting FAQ rich results” are all symptoms of the same move: keep the user on the SERP, answer the question there, and only send clicks when necessary.
Generative engines (Google, Perplexity, Claude, ChatGPT) don’t care about your pageviews. They care about:
- Completeness and clarity of the answer
- Perceived authority and safety of sources
- Speed and simplicity for the user
That means a growing share of your hard-won organic exposure will be:
- summarized (your content, their interface)
- re-ranked (your brand next to competitors, stripped of context)
- de-monetized (fewer clicks, more on-SERP answers)
2. Agents and recommendation systems are becoming the new “channel planners”
“Agent runtime wars” is not just an SEO curiosity. It’s a media buying problem.
When a user says to an AI assistant, “Plan my trip,” “Find me a project management tool,” or “What’s the best mattress under $800?” the agent:
- Decides which brands to consider
- Decides which criteria matter
- Often completes the purchase or drives the click
That’s a new kind of gatekeeper, sitting between your ad impressions and your revenue. It’s not neutral. It’s trained, steered, and often monetized.
3. Platforms are consolidating data and inventory into opaque “unified” systems
Look at:
- “Amazon Data Will Be Available on Netflix Inventory in the UK”
- “WTF is a unified ad platform?”
- “There’s a big shift: Amazon is turning the upfront into a pitch for its ad tech, not just primetime”
The pitch is always the same: “We’ll handle the complexity. Just give us budget and goals.”
Translation: you hand optimization power to a black box that optimizes to its own P&L.
Media buying is becoming less about choosing placements and more about setting constraints for someone else’s algorithm.
The uncomfortable truth: your current stack is built for the wrong era
Most marketing orgs are still architected around a 2015 reality:
- SEO teams optimize for blue links and snippets
- Paid teams optimize for platform-reported ROAS and last-click
- Brand teams optimize for human recall and sentiment
- Content teams write for people, then “SEO-ify” the headline
In an AI-intermediated world, this leaves three critical gaps:
- Nobody owns your “machine-facing” story. How your brand, products, and claims show up in AI summaries, knowledge graphs, and agent decisions is nobody’s explicit job.
- Your measurement is blind to interception. You don’t see how often you’re recommended but not clicked, mentioned but not linked, or summarized alongside cheaper competitors.
- Your incentives are misaligned. Teams are still rewarded on clicks and impressions, not on share of answers or share of recommendations.
A practical playbook: marketing to AI intermediaries without losing the plot
You don’t need another hype deck. You need a concrete operating model that treats AI and platforms as a first-class audience, without forgetting the human on the other side.
1. Create a “machine-readable brand spine”
AI systems are pattern matchers. If your brand shows up inconsistently across web, PR, product pages, and reviews, you’re training them to be confused.
Build a single source of truth that is:
- Structured: product attributes, pricing bands, use cases, integrations, industries, regions
- Canonical: one preferred way to describe what you do and who you’re for
- Public: expressed clearly on your site, in docs, and in structured data (schema.org, product feeds, APIs)
Then:
- Use schema markup aggressively for products, FAQs, how-tos, and org info even if individual rich results are deprecated; the structure still feeds models.
- Standardize naming conventions across site, app stores, marketplaces, and social bios.
- Audit third-party descriptions (directories, review sites, partner pages) and bring them in line with your spine.
2. Rewrite your content for “AI-first, human-verified” consumption
On-page AEO frameworks are a good starting point. The core principle: write so an LLM can extract a clean answer in one shot, then give humans a reason to stick around.
For key commercial topics:
- Open with a tight, self-contained answer paragraph that directly addresses the query.
- Use clear subheadings that map to common follow-ups (“pricing,” “implementation time,” “who it’s for,” “limitations”).
- State comparisons explicitly: “Compared to [Alt A] and [Alt B], [Your Brand] is best for X because Y.”
- Include concise, bullet-point pros/cons that are easy for models to lift.
Then, for humans:
- Add narrative, examples, and proof (case studies, numbers) below the core answer.
- Design pages assuming many visitors arrive already partially briefed by an AI summary; skip the 600-word preamble.
3. Treat “share of answers” as a new performance metric
You can’t manage what you don’t measure. Start tracking how often you show up in AI-generated recommendations and answers for your key jobs-to-be-done.
Practical steps:
- Define 20-50 high-intent prompts a real buyer would ask an AI assistant when in-market for your category.
- Regularly test these across major AI systems (Google’s AI features, ChatGPT, Claude, Perplexity, etc.).
- Record:
- Are you mentioned at all?
- In what context (leader, niche, budget, “also consider”)?
- Which competitors are co-mentioned?
- What claims are made about you (pricing, use cases, limitations)?
- Track this as Share of Answers (SoA) by topic and model.
This won’t be perfectly automated yet, but even a manual quarterly review will surface ugly surprises and obvious opportunities.
4. Build “agent-friendly” offers and surfaces
Agents like clarity and constraints. They need:
- Clear SKUs or plans
- Predictable pricing structures
- Machine-consumable availability and delivery data
- Simple, low-friction conversion paths
That means:
- Expose product and pricing data via feeds or APIs where possible (retail media, marketplaces, B2B directories).
- Reduce “call us for pricing” friction on core offers; ambiguous pricing is hard for agents to recommend.
- Standardize trial, demo, and consultation flows so “book a demo” is a one-click, predictable action an agent can trigger.
- Make sure your site performs well on basic technical metrics; agents are biased toward sources that load fast and don’t break.
5. Renegotiate your relationship with black-box media platforms
As Amazon, Netflix, and others roll out unified ad platforms with deep data, your instinct may be to pour budget into their automation and call it a day.
Resist the urge to fully outsource judgment. Instead:
- Define your non-negotiables: frequency caps, brand adjacency rules, geos you will not touch, margin floors.
- Design experiments that explicitly test platform-optimized vs. business-optimized outcomes (e.g., platform ROAS vs. contribution margin, LTV, refund rate).
- Demand transparency on inventory mix and data usage where your spend is material; you have more leverage than you think if you’re a top advertiser in a category.
- Keep a “manual lane” in each major channel where your team retains control over targeting and bidding to maintain internal expertise.
6. Put someone in charge of “AI intermediaries”
This can’t be a side quest for your SEO manager or a hobby for one curious PMM.
At minimum:
- Assign a clear owner (or small pod) responsible for:
- Maintaining your machine-readable brand spine
- Monitoring Share of Answers and agent recommendations
- Coordinating schema, feeds, and APIs with product and engineering
- Partnering with legal/comms on AI misrepresentation issues
- Give them a real KPI: e.g., improve SoA for top 20 commercial intents by X% over 12 months.
- Make them a required stakeholder on big content, site, and product launches.
The risk of ignoring this: you become “white label” in someone else’s UX
If you don’t actively market to AI intermediaries, three things tend to happen:
- You get genericized. Agents and answer engines describe you in bland category terms, stripping away your positioning and pricing power.
- You get commoditized. You’re surfaced as one tile among many, sorted by price, rating, or convenience, not by your strategic story.
- You get replaced. If your content is good but your brand is weakly represented in training data, someone else’s name gets attached to your ideas.
This is the real “AI’s trust problem” for marketers: not just whether users trust AI, but whether AI can be trusted to represent your brand fairly when you haven’t given it clear, consistent material to work with.
What to do this quarter
To keep this grounded, here’s a 90-day action plan for a CMO or performance lead:
- Run an AI visibility audit.
- Pick your top 20-30 commercial queries and jobs-to-be-done.
- Test them across 3-5 major AI systems.
- Document SoA, competitor set, and misstatements.
- Stand up a minimal brand spine.
- Define canonical descriptions, product attributes, and pricing bands.
- Update your homepage, key category pages, and “About” page to reflect this spine.
- Implement or clean up schema on those pages.
- Refactor 5-10 high-intent pages for AI-first readability.
- Add clear answer paragraphs, structured pros/cons, and explicit comparisons.
- Shorten intros and assume AI-prepped visitors.
- Instrument at least one new metric.
- Start tracking SoA manually or via internal tooling.
- On paid, add a business-grounded KPI (LTV, contribution margin) alongside platform ROAS.
- Appoint an owner.
- Pick the person or pod responsible for AI intermediaries.
- Give them time, not just a Slack channel.
The platforms and models will keep changing. The principle won’t: you are no longer marketing only to people; you’re marketing to the systems that decide what people see. Treat those systems as a real audience, and you’ll still be in the consideration set when the agents start doing the shopping for us.