Marketing to machines? The new reality of agentic AI (and why humans still matter)
A CMO at a midsize enterprise software company had a moment recently.
Her team spent three years building top-of-page-one SEO dominance. Then someone ran a simple test: typed the company's core use case into ChatGPT and asked for vendor recommendations.
The company didn't appear. Not once. "We thought we had search figured out," she said. "Turns out we optimized for the wrong engine"
That scenario is playing out across industries. Most organizations haven't caught up.
The numbers that explain the shift
Generative AI has moved from novelty to primary research channel faster than almost any technology before it. ChatGPT alone has over 900 million weekly active users. Google AI Overviews now appear in more than 25% of all searches, up from 13% just a year ago .
Forrester's 2025 Buyers' Journey Survey found that generative AI is now the single most cited interaction type for purchase research—ahead of vendor websites, peer recommendations, and analyst reports .
But here is the number that should scare you: only 8% of ChatGPT's citations and 8.6% of Google Gemini's citations come from URLs that appear in Google's top 10 organic results for the same query .
Roughly 92% of top Google results do not appear when AI answers the same prompt. The SEO playbook does not carry over to GEO.
The two targets: machine and human
We are rapidly moving from an era of "humans who browse" to "agents that transact" . Consumers will increasingly stop searching for products themselves and instead delegate the task to AI .
When a consumer asks an AI to "buy the best toaster," the algorithm makes the decision based on data, not necessarily brand sentiment. This forces a strategic pivot from traditional SEO to Generative Engine Optimization (GEO) .
Success is now measured by brand citations and the share of traffic generated by LLMs. Brands must ensure their content is machine-readable and prioritized by LLMs.
But here is the tension. 75% of global marketers express excitement about generative AI, but there is a growing philosophical debate about the enduring value of human connection
The authenticity paradox
The acceleration of AI has created an equal and opposite reaction: a craving for the real. As generative AI floods the internet with synthetic content, "perfection" is no longer a differentiator; it is a commodity. The new premium is flaws, vibe, and human connection .
Ogilvy's 2026 Futures report puts it plainly: "When everything can be faked, reality becomes a luxury" . Consumers are gravitating toward creators and community-led forums because they represent a verifiable human pulse in a synthetic world .
This is the AI search paradox: The more AI-generated content exists, the more valuable human thinking becomes
What the data says about the human premium
AI-generated content gets more impressions. It does not get more clicks. Research shows that organic click-through rates dropped by 61%, while paid ad engagement declined by 68% .
AI referrals are projected to account for just 1% of total site traffic. Those visitors, however, convert at five times the average rate . AI-driven users are not browsing but deciding.
AI-generated content is flooding the web, but AI-powered search systems are deprioritizing content that AI helped create. Instead, search systems surface work demonstrating originality, firsthand insight, and human judgment .
The conclusion is uncomfortable but clear: Stop measuring success by output alone. Volume no longer equals influence. Invest in thinkers, subject-matter experts, operators, and practitioners are now strategic assets. Shift from optimization to credibility
How Rankfender bridges the two worlds
We built Rankfender to help brands navigate this new reality.
RAIVE monitors citations across 7 AI systems daily. You see where your brand is cited and where it is invisible. Not clicks. Citations.
RAISA watches your GSC and GA4 and tells you what to do next. Not just "here is what happened." "Here is your top priority this week."
RCGE generates content optimized for both Google rankings and AI extraction. Structured. Conversational. Original. But the human always adds the examples, the data, the voice.
The AI handles the repetitive work. The human handles the judgment. That is the only way to scale without losing distinctiveness.
What I am curious about
What is your biggest challenge with the shift from clicks to citations? Are you marketing to the machine, the human, or both?
Imed Radhouani
Founder & CTO – Rankfender
Replies
The "AI handles the reps, human handles the judgment" line is the same thing I keep landing on with my own product. So we agree on the core.
The stat that sticks: 92% of top Google results don't show when AI answers the same prompt. If that holds, a lot of teams are about to learn their three years of SEO optimized the wrong engine, exactly like your CMO.
One flag though: some of these numbers feel cleaner than reality. The 5x conversion and 61% CTR drop especially. AI search shifts month to month as engines swap models, so a stat from one quarter goes stale fast. I believe the direction. I'd hold the decimals loosely.
The part I'd argue with is "marketing to machines." When an AI picks the best toaster, it's still pulling from what real humans said in reviews and Reddit threads. So you're not marketing to the machine, you're marketing to the humans the machine reads. Which loops right back to your authenticity point.
Both, to answer you. But the human's the leverage. The machine just decides who gets surfaced. The human decides whether anyone trusts it after they click.
memi
This feels like the next version of SEO, but less keyword gaming and more structured clarity. If an agent cannot tell what your product does in one clean pass, a buyer probably cannot either.
The 92% number is real but reads stronger than it is because it lumps commercial-intent prompts in with informational ones. The deeper structural bias in LLM citations isn't anti-SEO, it's pro-old-structured-long-form: Wikipedia, docs, manuals, papers. Which means the "GEO" pivot for most builders looks less like rank optimization and more like becoming citable: schema.org structured data, Wikipedia presence, and stable canonical URLs that haven't moved. SemRush published a useful citation-bias breakdown by prompt category earlier this year (https://www.semrush.com/blog/ai-overviews/).