The shift nobody’s budgeting for: from “searching” to “delegating”
The most important change in marketing right now isn’t another channel or format. It’s behavioral:
users are moving from searching to delegating.
Google’s AI Overviews, AI Mode, semantic search, “user intent extraction,” agentic assistants, AI voice agents – all of these point in one direction:
people are increasingly saying, “Just do it for me” instead of “Show me 10 blue links.”
For CMOs and media buyers, this is not an SEO story. It’s a full-funnel acquisition story:
AI intermediaries are sitting between your spend and your customer, deciding who gets surfaced, recommended, or actioned.
If you’re still optimizing for clicks and last-click ROAS while ignoring how AI systems “see” and “trust” your brand, you’re operating with an outdated model of demand capture.
What’s actually changing: from results lists to AI gatekeepers
Look at the headlines:
- “From searching to delegating: Adapting to AI-first search behavior”
- “How to Track AI Overviews: Mentions, Citations, Click Loss, and the Traffic Google Won’t Show You”
- “Semantic Search Is the Only Search That Matters Now (For SEO and AI Visibility)”
- “Google’s New User Intent Extraction Method”
- “Recommended or Rejected: Does AI Trust You”
- “AI Voice Agents: How to Get Started”
Put together, they describe a new reality:
- Users outsource more of the journey to AI systems (search, chat, voice, agents).
- AI systems summarize, filter, and often act on behalf of the user.
- Visibility is no longer just “ranked” – it’s selected or omitted by an AI layer.
- Traditional analytics under-report what’s happening because the AI sits between you and the click.
The job is no longer “get found in search.” It’s “get picked by the AI that’s doing the searching for your customer.”
The new funnel: human on top, AI in the middle, you at the bottom
The classic funnel assumed a human moving step by step:
awareness → consideration → evaluation → purchase.
In an AI-first world, the middle of that funnel is increasingly automated:
- Awareness: Still human-driven – social, OOH, creator content, word-of-mouth, PR, brand.
- Delegation: User asks an AI: “Find me the best X for Y,” “Book me Z,” “Compare A vs B,” “Draft an RFP.”
- AI Mediation: The AI parses options, filters sources, and proposes 1-3 “answers” or actions.
- Action: Click, call, cart add, sign-up, demo request – often pre-filtered by the AI.
That “AI Mediation” layer is where your acquisition model is now at risk.
If you aren’t explicitly designing for it, you’re competing in a game whose rules you’re not even reading.
What this breaks in your current operating model
1. Your definition of “organic” and “paid”
AI Overviews and assistants blur the line:
- Organic pages might be cited but not clicked.
- Paid results might appear below an AI answer that satisfies the query.
- Brand mentions may show up in AI text without referral data.
You can’t treat SEO, content, and paid search as separate lanes anymore.
To the AI, they’re just signals about whether you’re a credible answer.
2. Your measurement stack
Ahrefs is already writing about “the traffic Google won’t show you.”
AI Overviews and assistants:
- Reduce visible impression and click data.
- Mask your role in consideration (you influenced the answer but didn’t get the visit).
- Shift value to “unattributed direct” and brand search that looks like magic but isn’t.
Meanwhile, CFOs are cutting AI budgets and asking for three metrics that matter.
If you can’t show how AI-influenced behavior maps to revenue, your “AI initiatives” look like cost centers.
3. Your creative and content strategy
AI systems don’t “read” your content like humans do. They:
- Parse entities, relationships, and claims (semantic search).
- Prefer structured, consistent, corroborated information.
- Down-rank vague, fluffy, or contradictory messaging.
The Copyhackers line – “AI’s trust problem: The cost of outsourcing your message” – is the other side of this:
if you outsource your own message to generic AI, you become semantically identical to everyone else.
The systems can’t tell why you matter.
What AI systems actually “trust” (and how to become the default answer)
Forget the mystical talk about “AI alignment.” For commercial systems, trust boils down to a few practical dimensions:
1. Semantic clarity: can the AI tell what you’re actually about?
From the semantic search work and Google’s user intent extraction:
- Use precise language about who you serve, what you do, and for which use cases.
- Structure content around real tasks and intents (“for enterprise IT teams migrating X,” “for DTC brands with AOV above $Y”).
- Align your site architecture with topics and entities, not just product names.
Operatively: your content briefs should start from intents and entities, not keywords.
2. Evidence density: can the AI back up recommending you?
Systems like AI Overviews pull from multiple sources and prefer well-cited, consistent claims.
That means:
- Publish specific, quantified claims with clear context (“37% increase in inquiries in 90 days for B2B services with >$5M revenue”).
- Support those claims across multiple surfaces: case studies, third-party mentions, reviews, PR, social proof.
- Keep your data consistent across your site, listings, and partner content.
You’re not just convincing humans anymore; you’re feeding a machine that cross-checks you.
3. Behavioral reliability: do users who pick you behave like the model expects?
AI systems optimize for user satisfaction and downstream metrics:
- If users who click your result bounce, the model learns you’re a bad answer.
- If users who call you complain or churn, that signal can show up via reviews and ratings.
- If your landing pages are slow, confusing, or misaligned with the query, your “trust score” drops.
This is where CRO and product experience quietly become media buying inputs.
If you don’t fix the post-click journey, AI intermediaries will fix it for you by sending traffic elsewhere.
How to adapt your acquisition strategy to AI-first behavior
1. Redesign your funnel map with an “AI Mediation” layer
Don’t treat this as a thought exercise. Literally redraw your funnel and add:
- Human intent: “I need X,” “I’m stuck with Y,” “I want to switch from Z.”
- AI interface: Where that intent is expressed (search, chat, voice, in-app assistant).
- AI decision: What the system does – summarize, recommend, compare, book, call.
- Brand presence: Where and how you can appear in that decision (cited source, structured data, ads, integrations, partnerships).
Then align your channels:
- SEO/content: optimized for intents and entities that feed AI.
- PPC: structured to complement, not fight, AI answers (e.g., offer depth where AI is shallow).
- Partnerships/integrations: be the default option inside vertical assistants and tools.
2. Build an “AI visibility” reporting layer
You won’t get perfect data, but you can get directional signal. Start with:
- AI Overview presence: Track when your brand or URLs appear in AI Overviews for priority queries.
- Brand lift in assisted channels: Monitor trends in branded search, direct traffic, and referral sources after major AI feature rollouts.
- Query mix shifts: Watch for more conversational, task-based queries in your search terms and site search.
- Downstream performance: Compare conversion and LTV for cohorts acquired from AI-heavy surfaces vs. traditional SERPs or social.
At the CMO-CFO table, you want to say:
“We’re seeing X% of new customers come from AI-mediated journeys, with Y% higher LTV.
Here’s what we’re investing in to be the default recommendation.”
3. Treat AI systems as a new “channel partner,” not a black box
You already adapt to algorithms: Meta, TikTok, Pinterest, Performance Max.
AI intermediaries are just more opinionated partners.
Operationally:
- Assign ownership: who in your org “owns” AI visibility and assistant behavior?
- Run experiments: use tools like Google’s Experiment Center to test how changes in creative, landing pages, and feed structure affect AI-driven placements.
- Document patterns: when you see your brand cited or recommended, reverse-engineer why – structure, language, schema, reviews, or external mentions.
4. Tighten your message so AI can’t confuse you with everyone else
In a semantic world, “we help businesses grow faster with AI-powered solutions” is not positioning; it’s noise.
You need:
- Category clarity: Be unambiguous about what you are and are not.
- Use-case specificity: Spell out concrete jobs-to-be-done in language a model can parse.
- Audience boundaries: Define who you’re not for as clearly as who you are for.
This is not just brand work; it’s an input into how AI systems cluster and recommend you.
5. Rebalance budgets toward “being picked,” not just “being seen”
If users are delegating, impressions matter less than inclusion in the AI’s short list.
That changes where money should go:
- More into structured content, data quality, and technical hygiene that feed AI systems.
- More into reviews, UGC, and third-party validation that models read as trust signals.
- More into experimentation with AI-native surfaces (AI voice agents, in-product assistants, agentic advertising pilots).
- Less into chasing marginal incremental clicks on generic queries that AI will increasingly answer directly.
What to do in the next 90 days
If you’re running a marketing or growth team, here’s a concrete 90-day plan to stop flying blind.
Week 1-2: Map and audit
- List your top 50-100 revenue-driving queries, tasks, and intents.
- Search them in AI Overviews, chat-style interfaces, and voice where possible.
- Document:
- Are you cited? How often?
- Which competitors are consistently recommended?
- What content formats and structures show up?
- Audit your top landing pages for:
- Semantic clarity (entities, use cases, audiences).
- Structured data and technical health.
- Consistency of claims and proof points.
Week 3-6: Fix the obvious gaps
- Rewrite key pages to make intent, audience, and value explicit in plain language.
- Add or correct schema and structured data for products, FAQs, reviews, and organization details.
- Standardize your core claims across site, decks, and profiles so models see a consistent story.
- Patch high-bounce, high-intent pages with basic CRO: faster load, clearer CTAs, reduced friction.
Week 7-10: Test into AI-informed media buying
- Use Google’s Experiment Center or equivalent to:
- Test more specific, intent-rich ad copy vs. generic benefit copy.
- Test landing pages aligned to distinct intents (e.g., “switching from competitor X” vs. “first-time buyer”).
- Shift a small portion of budget (5-10%) to:
- AI-native surfaces (assistant integrations, voice agents, in-tool placements).
- Programs that increase high-quality reviews and public proof.
- Set up a simple AI visibility dashboard:
- Manual tracking of AI Overview presence for your top queries.
- Trend lines for branded search, direct traffic, and assisted conversions.
Week 11-13: Turn it into an operating rhythm
- Add “AI mediation impact” to your monthly marketing review:
- Where are we being picked or skipped?
- What changed after we updated content or structure?
- Give one leader explicit accountability for AI visibility and assistant behavior.
- Align with finance on a small but protected AI experimentation budget tied to clear revenue hypotheses.
The platforms are already optimizing for a world where customers delegate.
The question is whether your marketing organization is optimizing for a world where AI, not the user, decides if you get a shot.