The real shift: you’re no longer marketing to humans first
Scan those headlines and you’ll see the same pattern hiding in plain sight:
- AI Overviews and “AI Mode” reshaping search results
- LLM-focused SEO and entity-based SEO
- AI WordPress plugins for internal linking
- ChatGPT’s native shopping and AI-powered sales workflows
- Chatbots as front-line customer care
The common thread: your marketing is increasingly being consumed, filtered, summarized, and ranked by machines before it ever reaches a human.
That’s the issue that matters for operators right now. Not “AI in marketing” as a buzzword, but the rise of the AI intermediary:
- AI search layers deciding if your brand is even mentioned
- AI shopping agents picking which products to recommend
- AI inbox filters deciding what gets seen or clipped
- AI social feeds compressing and remixing your content
If you’re a performance marketer or media buyer, the question is no longer “How do I persuade a user?” but “How do I become the default answer chosen by the machine that stands between us?”
Why this matters more than another channel tactic
You can ignore a new social network. You can sit out a creative trend. You cannot sit out the AI intermediary because:
- It’s cross-channel. Search, email, social, on-site, commerce – all are getting AI layers.
- It’s default-on. Users don’t have to “opt in” to AI summaries or recommendations. Platforms quietly turn them on.
- It compresses the funnel. AI answers collapse research steps. Fewer clicks, fewer tabs, fewer chances for you to intercept.
- It rewards structure, not just creativity. The brands that win are the ones machines can easily parse, classify, and trust.
The upside: this is a rare chance to build a durable edge. Most advertisers will keep optimizing for human eyes only. You can optimize for both the human and the machine.
The AI intermediary stack: where your work is being reinterpreted
1. Search: from “10 blue links” to “one synthesized answer”
AI Overviews, “AI Mode,” and LLM-powered SERPs are doing three things that matter to you:
- Answer compression: Instead of 10 results, users see one synthesized block with a couple of citations.
- Brand de-emphasis: The answer is framed as “what’s true,” not “what Brand X says.” Your logo may never appear.
- Entity-first ranking: Search is shifting from keywords to entities: brands, products, people, places, and their relationships.
Translation: your job is not just to rank for “best running shoes,” but to ensure your brand is a recognized, trusted entity that AI systems feel safe citing.
2. Commerce: AI as your new category manager
Native shopping inside ChatGPT and similar tools will behave like a ruthless, always-on category manager:
- It will pick a small set of “safe” products to recommend.
- It will optimize for user satisfaction and refund risk, not your ROAS.
- It will remember what converted and bias toward those brands.
If you’re not in that short list, your ads and content are fighting upstream against the assistant’s “default picks.”
3. Owned channels: AI as editor, gatekeeper, and spam cop
Email providers, CRMs, and even browsers are layering AI on top of your messages:
- AI summaries of long emails
- Smart categorization of “promotional” vs “important”
- On-the-fly rewriting or truncation of subject lines and previews
If 73% of ecommerce emails are already “broken,” as one headline claims, AI filters are about to make that pain visible. The sloppier your structure, the more likely your message gets misclassified or compressed into irrelevance.
4. Social and support: bots as your first impression
Chatbots and AI support tools are no longer just cost-saving widgets; they’re often the only interaction a prospect has before buying:
- They decide which product to recommend.
- They decide whether to escalate to a human.
- They decide what “your brand” sounds like in real time.
That’s not a UX detail. That’s performance marketing by another name.
Designing for the AI intermediary: a playbook for operators
You don’t need another “AI in marketing” think piece. You need an operating system. Here’s a practical way to adapt your stack.
1. Make your brand machine-readable as an entity
If AI systems can’t confidently understand who you are and what you sell, they won’t recommend you. Fix that first.
Actions for the next 90 days:
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Standardize your naming. Use one canonical brand name and product names across:
- Site (titles, H1s, footers)
- Social profiles
- Marketplaces
- Press and partner sites
Inconsistent naming = fragmented entity = weaker AI visibility.
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Implement structured data properly.
- Use schema.org markup for Organization, Product, FAQ, Article, and Review where relevant.
- Make sure NAP (name, address, phone) and URLs match across all instances.
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Build a clean “source of truth” page for each entity.
- One page per product or service, clearly titled, with specs, pricing ranges, and use cases.
- Link to it consistently from internal pages and campaigns.
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Earn citations that look good to machines.
- Push for mentions in industry directories, comparison sites, and credible blogs with your exact brand and product names.
- Prioritize sources that LLMs are likely trained on (Wikipedia-type, high authority, editorial content).
2. Write for humans, format for machines
AI systems don’t “read” like humans. They parse patterns. You can keep your voice and still give them structure.
On-site and content changes that matter:
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Lead with clear, atomic answers.
- Open key pages and articles with 1-3 sentence answers to the main question.
- Use simple, explicit language: “[Product] is a [category] that helps [audience] do [job].”
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Use consistent headings and FAQs.
- Turn common queries into H2/H3 questions and direct answers.
- Add FAQ sections that mirror how users actually ask questions, not how you wish they did.
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Stop cannibalizing your own topical authority.
- Audit overlapping content targeting the same intent.
- Consolidate into fewer, stronger pages instead of dozens of near-duplicates.
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Keep timestamps honest and content fresh.
- Update high-intent content meaningfully, then update the publish/modified dates.
- Don’t fake freshness; AI systems can often detect shallow edits.
3. Train your AI “employees” like you train your human ones
If you’re deploying chatbots, AI support, or AI sales assistants, treat them as junior staff, not magic boxes.
Minimum viable governance:
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Give them a brand and product playbook.
- Document positioning, pricing rules, guarantees, and “never say this” phrases.
- Feed that into your chatbot/assistant as system instructions or knowledge base content.
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Define guardrails around offers.
- Set explicit limits on discounts, refunds, and promises the AI can make.
- Review transcripts weekly to catch drift or hallucinated offers.
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Instrument them like a campaign, not a tool.
- Track conversion rate, AOV, CSAT, and escalation rate for AI-led sessions.
- Compare against human benchmarks and iterate prompts and knowledge where gaps appear.
4. Rethink performance measurement in an AI-shaped funnel
AI intermediaries blur attribution. Users see your brand in a summary, then convert via a branded search, then click an ad. Who gets credit?
Practical steps to avoid flying blind:
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Track “assist” metrics, not just last-click ROAS.
- Monitor branded search volume, direct traffic, and view-through conversions alongside your usual KPIs.
- Expect some channels (e.g., organic content) to drive more “assists” than direct last-click conversions.
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Use incrementality testing where AI layers are strong.
- Geo-split or audience-split tests in markets where AI Overviews are heavily rolled out.
- Measure lift from content and upper-funnel campaigns that are likely feeding AI answers.
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Watch for “invisible” cannibalization.
- If AI answers start surfacing your content, you may see fewer clicks but higher-quality traffic.
- Track revenue per session and conversion rate, not just sessions.
5. Build creative that survives summarization
AI systems love to summarize. That’s a threat if your message only works as a 90-second video or a 2,000-word post. It’s an opportunity if your core message is sharp enough to survive compression.
Creative principles for the AI era:
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Make your “one sentence” painfully clear.
- Every campaign should have a single, literal sentence that a bot could quote to explain you.
- Use that sentence in copy, meta descriptions, and scripts.
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State your proof, not just your promise.
- “37% more inquiries in 90 days” is something an AI will happily repeat.
- “Game-changing growth” will be ignored or paraphrased into mush.
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Design assets that are easy to describe.
- Clear product shots, readable overlays, and obvious use cases help AI vision models understand what’s going on.
- Aesthetic chaos may be fun, but it’s harder for machines to classify and recommend.
What to actually do this quarter
If you’re running growth, media, or performance, here’s a simple quarterly plan to get ahead of the AI intermediary curve.
Month 1: Fix the foundations
- Audit naming consistency and structured data across site and key profiles.
- Consolidate overlapping content around your top 10 commercial intents.
- Define your “one sentence” positioning and bake it into core pages and ads.
Month 2: Tune for AI discovery and answers
- Rewrite key pages to lead with atomic answers and clear FAQs.
- Add or clean up schema for products, FAQs, and reviews.
- Start a small, focused push for high-authority citations using your exact brand and product names.
Month 3: Train and measure your AI layer
- Document brand, offer, and product rules for any chatbots or AI assistants.
- Instrument AI-led interactions with conversion and satisfaction metrics.
- Run at least one incrementality test in a segment where AI search or AI support is heavily used.
The platforms will keep changing. The AI features will keep shipping. The operators who win won’t be the ones chasing every new toggle; they’ll be the ones who accept a simple, uncomfortable reality:
You are now marketing to two audiences at once – humans and machines. The sooner your strategy reflects that, the less you’ll be at the mercy of the next “AI update” headline.