The real shift: from “searching” to “delegating”
Look past the AI hype cycle and the headlines point to one hard shift: people are no longer “searching” in the classic sense. They’re delegating.
Google’s AI Overviews, AI Mode, semantic search, “user intent extraction,” agentic advertising, AI voice agents, AI-first CRM, Threads and Pinterest algorithms, Performance Max for B2B, Reddit strategies – they’re all symptoms of the same change:
Attention is moving from results pages to AI intermediaries that decide what to show, what to summarize, and what to act on.
That has three brutal consequences for CMOs, performance marketers, and media buyers:
- Your traffic and attribution models are lying more than ever.
- Your “content” is increasingly consumed by machines, not humans.
- Your budget approvals now depend on whether AI systems – and CFOs – trust you.
The operators who win the next three years won’t be the ones who “use more AI.” They’ll be the ones who treat AI systems as a new channel with its own rules of trust, visibility, and economics.
AI is the new distribution channel (you just don’t control it)
In classic search, you fought for blue links. In social, you fought for feed placement. In retail, you fought for shelf space.
In AI-first environments, you’re fighting for inclusion in the answer – or in the action the AI takes on the user’s behalf.
Consider three patterns from the headlines:
- AI Overviews & semantic search: Ahrefs and Moz are now writing playbooks on “how to rank in AI Overviews” and “semantic search is the only search that matters.” Translation: the unit of competition is no longer a keyword and a page; it’s an intent cluster and an answer set.
- AI voice agents & AI CRM: Sales and support flows are being mediated by AI agents that choose what to say, what to show, and when to escalate. Your copy, offers, and product data are being consumed by these agents first, humans second.
- Performance Max, Experiment Center, algorithmic social: Google Ads’ Experiment Center, PMax for B2B, Threads/Pinterest/Reddit algorithm guides – all are about feeding black-box systems the right signals and constraints, not micromanaging every placement.
The operating question is no longer “How do I get more clicks?” It’s:
“How do I become the default answer or action when an AI is doing the choosing?”
AI trust is the new quality score
Social Media Examiner asks “Does AI trust you?” and Adweek says “Trust is a performance metric and CMOs are the owners.” That’s not philosophy; it’s targeting math.
Every AI system that mediates your reach is running some version of the same internal logic:
- Is this source accurate?
- Is it consistent with other sources?
- Does it produce good downstream outcomes (low bounce, high satisfaction, low refunds, etc.)?
- Is it safe to show or recommend?
In paid media, you already know this as Quality Score, conversion modeling, and brand safety. In AI-first environments, that logic expands:
- Your structured data and product feeds become a trust signal.
- Your content consistency across channels becomes a trust signal.
- Your user outcomes (returns, complaints, churn) become a trust signal.
- Your legal and privacy posture becomes a trust signal – see Google’s $68M voice assistant settlement and the EU’s scrutiny of deepfakes.
If you’re still thinking “SEO,” “paid,” “social,” “CRM” as separate silos, you’re behind. To an AI system, it’s all one big graph of: Does this brand do what it says, and do users end up happy?
Why CFOs are cutting AI budgets – and what they’re actually asking you
Search Engine Journal is already reporting that CFOs are cutting AI budgets and only three metrics are saving them. At the same time, Retail Dive is warning that “retail’s risky AI commerce bet” is very real.
The CFO’s view is simple:
- AI spend is showing up as Opex bloat, not clear incremental revenue.
- Marketing teams are piloting tools without clear integration into core funnels.
- Most AI projects are framed as “innovation” instead of “profit engines.”
Underneath the budget cuts is a blunt question:
“Can you prove that AI-mediated distribution is making us money, not just making decks?”
To answer that, you need to stop treating AI as a line item and start treating it as the infrastructure of your funnel.
From “funnel with AI sprinkled on top” to “AI-native funnel”
Neil Patel’s content on “How to adapt your entire marketing funnel with AI” is pointing in the right direction, but most implementations are still surface-level: AI-written blogs, AI ad variants, AI chatbots.
An AI-native funnel is different. It assumes that:
- Discovery is mediated by AI search and social algorithms.
- Consideration is mediated by AI comparison, summarization, and review aggregation.
- Conversion is mediated by AI agents (on-site, in CRM, in support) that can nudge, cross-sell, and recover.
- Retention is mediated by AI-driven lifecycle messaging and product experiences.
Practically, that means re-architecting four layers of your operation.
1. Data layer: feed the machines like they’re your biggest channel (because they are)
If AI systems are your new distribution, your job is to feed them clean, rich, consistent data.
- Fix your schemas and feeds. Product schema, FAQ schema, event schema, reviews, pricing, inventory – if it’s not machine-readable, it might as well not exist. This is not “SEO hygiene”; it’s distribution infrastructure.
- Standardize your claims. If your landing pages, product pages, and sales decks say different things, AI summarizers will surface the contradictions. That erodes trust scores.
- Instrument outcomes, not just clicks. Connect ad platforms, analytics, CRM, and support so you can report on AI-touched journeys: sessions that interacted with AI chat, AI recommendations, or AI-generated content.
2. Content layer: write for humans, structure for machines
Ahrefs is right: semantic search is the only search that matters now. Your content needs to be:
- Clustered by intent, not by keyword. Build topic clusters that map to “jobs to be done,” not vanity phrases. AI Overviews pull from coverage of a topic, not a single page.
- Explicitly answer questions. Clear headings, direct answers, pros/cons, comparisons, pricing ballparks. AI summarizers reward clarity and structure.
- Backed by evidence. Case studies (like Moz’s 37% inquiry lift), benchmarks, and methodology sections are catnip for AI systems looking for authoritative sources.
Your copy team’s new brief: “Assume your primary reader is an AI summarizer that decides whether humans ever see this.”
3. Media layer: trade control for constraints and experimentation
With Performance Max, Experiment Center, and algorithmic social feeds, you are no longer buying placements; you’re setting constraints and feeding signals.
To stay in control of outcomes:
- Define allowed and forbidden outcomes. For PMax and similar, set hard guardrails: negative audiences, geos, placements, and conversion definitions that actually match profit, not just form fills.
- Use Experiment Centers aggressively. Treat experiments as your only reliable visibility into black-box behavior. Design tests around incrementality, not just CPA.
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Measure “AI assist” impact. For campaigns where AI is doing creative or bidding, track the delta in:
- Time to launch
- Number of variants tested
- Stable ROAS / CAC after ramp
These are the metrics CFOs can understand and fund.
4. Experience layer: make your brand the “safe default”
When users delegate, they’re often saying: “Just pick something good for me.” AI systems respond by steering toward options that look safe and satisfying.
To become that default:
- Reduce post-click friction. The Moz case study on title tag rewrites is really about this: aligning expectations between snippet and page. AI-driven clicks are even less tolerant of mismatch.
- Instrument and improve “regret events.” Refunds, cancellations, angry tickets, unsubscribes – these are the outcomes AI systems quietly learn to avoid. Treat them as media signals, not just ops problems.
- Build visible proof of reliability. Public SLAs, transparent pricing, clear policies, and consistent support experiences become part of the trust graph that AI systems crawl.
New metrics for an AI-mediated world
Your current dashboard is probably built for a world of direct clicks and last-touch attribution. That world is fading.
To survive CFO scrutiny and AI opacity, add a second layer of metrics that answer three questions: Are we visible? Are we trusted? Are we profitable?
1. Visibility metrics
- AI Overview presence rate. Percentage of priority intents where you appear in AI Overviews, not just classic SERPs.
- AI citation share. Among pages cited in AI summaries for your category, what share are yours?
- Algorithmic feed inclusion. Share of impressions coming from “recommended” or “for you” surfaces vs direct followers.
2. Trust and quality metrics
- Post-click satisfaction. Time to first value, scroll depth, task completion rates on AI-driven sessions (chat, AI search, AI recommendations).
- Outcome quality. Return rates, complaint rates, churn for cohorts acquired or assisted by AI-driven campaigns vs baseline.
- Content consistency score. Regular audits of claim alignment across site, ads, CRM, and sales materials.
3. Economic metrics
- AI-assisted revenue. Revenue from journeys where AI touched discovery, consideration, or conversion – even if not last-click.
- AI productivity delta. Hours and cost saved in creative production, testing, and optimization vs pre-AI baselines, tied to performance outcomes.
- Incremental profit per AI dollar. Not “ROI of AI,” but incremental profit generated by AI-mediated improvements in existing channels.
What to do in the next 90 days
You don’t need a five-year AI roadmap. You need a 90-day operating plan that moves you from “sprinkling AI” to “competing in an AI-mediated market.”
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Pick 10-20 high-value intents. Use search data, CRM, and sales input to define the core jobs people hire you for. Audit:
- Do you appear in AI Overviews for them?
- Do you have clear, structured content that answers them?
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Run a trust and consistency audit. For those intents, compare claims across:
- Top landing pages
- Ad copy
- Sales decks
- Help center / support macros
Fix contradictions and vague promises.
- Harden your data layer. Implement or fix schemas, product feeds, and event tracking. Ensure AI chat, recommendation engines, and CRM all write back to a shared analytics layer.
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Redesign one key campaign as AI-native. For a flagship product or segment:
- Use PMax or equivalent with strict guardrails.
- Feed it high-quality creative and audience signals.
- Measure incremental lift and AI productivity delta, not just CPA.
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Reframe your AI story for the CFO. Stop talking about “AI initiatives.” Start reporting:
- AI-assisted revenue
- Cost savings in media and creative ops
- Risk reduction (brand safety, fraud, compliance)
The platforms are already optimizing for a world where users delegate. Your job is to decide whether your brand is the thing they delegate to, or the thing the AI quietly routes around.