AI search is already influencing buying decisions, yet only 16% of brands systematically track performance inside AI-driven results. That gap won’t stay open for long.

Here’s the part that should make any demand gen leader pause: only 16% of brands systematically track AI search performance, even as 40–55% of consumers in key sectors use AI-based search to make purchasing decisions (Source: [1]).

That mismatch is the story of AI-driven search in 2026. Not “SEO is dead.” Not “everything is prompts now.” Just a basic operational gap: buyers are changing behavior faster than most teams are changing measurement.

SMX Now’s first event on April 1, featuring iPullRank’s session “AI Search Picks Winners: Here’s the GEO Strategy Behind It,” lands in the middle of that gap. The timing makes sense. The numbers do too.

Because this isn’t a theoretical shift. Google’s AI Overviews are already reshaping discovery, pulling information from multiple sources—business websites, yes, but also third-party platforms like Yelp and Reddit (Source: [1]). And when an AI answer becomes the product, “rankings” stop being the only scoreboard that matters.

The metric that quietly broke: clicks

AI-driven search changes the economics of informational queries. If an AI summary answers the question directly, traditional click-through can fall—meaning a #1 ranking can still produce a disappointing outcome unless the brand is cited or used as a source in that answer (Source: [1]).

That’s the pattern interrupt for anyone raised on blue links: visibility is no longer a synonym for traffic. Sometimes it’s the opposite.

And the exposure is rising. One analysis of 21.9 million queries found AI Overviews appearing in 25.11% of Google searches, up from 13.14% in March 2025 (Source: [2]). That’s not a niche feature. That’s a new default for a meaningful slice of queries.

But the more unsettling detail is volatility. In early 2026, brand visibility in AI results reportedly dropped 35.9% (from 1.92% to 1.23%) over a short period, while citation rates dropped 34.4% (from 7.35% to 4.82%) (Source: [2]). One week you’re “in the answer.” The next week you’re not. No site changes required.

That is what makes measurement the first adaptation, not the last.

GEO/AEO sounds new. The risk isn’t.

The industry has started calling this shift GEO (Generative Engine Optimization) or AEO (AI Engine Optimization): the work of making content and brand signals easy for AI systems to cite, summarize, and recommend (Sources: Expert opinions [2][6]; Latest developments [2]).

But here’s the cognitive dissonance: plenty of what “works” still looks like classic SEO. Some agency leaders argue AI search remains 80–90% rooted in the fundamentals, and that digital PR and brand authority matter more than hype—especially while ROI measurement is still early (Source: Expert opinions [3]).

Both ideas can be true. The mechanics of crawling, understanding, and trusting information haven’t vanished. What changed is where the systems get their confidence from—and how often your own website is even the main input.

In fact, some reporting suggests up to 85% of what AI recommends can come from third-party content rather than a company’s owned channels (Source: Latest developments [2]). That’s an uncomfortable number for teams still treating the website as the only “source of truth” that matters.

A practical adaptation plan for demand gen teams

For a VP of Demand Gen, the question isn’t whether AI search is “real.” It’s whether pipeline plans can absorb a discovery shock. Some estimates put potential traffic declines from traditional channels at 20–50% for brands that are unprepared for AI-powered search shifts (Source: [1]). Even the low end hurts.

So what does adaptation look like when the ground is moving? Start with a dual-track plan: protect classic search capture while building AI visibility. Traditional search isn’t gone; 95% of Americans still use search engines like Google each month (Source: Latest developments [1]). The smarter move is additive, not replacement.

From there, the playbook becomes operational. Three priorities tend to separate teams who “talk AI” from teams who can manage it:

There’s also a risk angle that doesn’t get enough airtime. When 67% of consumers aren’t rigorously fact-checking AI sources before choosing a local business, hallucinations and misinformation become revenue problems, not PR problems (Source: Latest developments [1]). Accuracy workflows—consistent listings, rapid correction processes, monitoring—belong on the same dashboard as CPL and pipeline.

Why SMX Now fits this moment

Events like SMX Now matter less as “trend briefings” and more as forcing functions. AI search is already big enough to distort attribution, but still messy enough that teams can hide behind ambiguity.

That ambiguity won’t last. GenAI use in shopping-related behavior grew 35% from February to November 2025 (Source: [3]). ChatGPT alone reportedly sits at 400.61 million monthly active users worldwide (Source: [6]). Meanwhile, marketer adoption is high—69.1% say they’ve integrated AI into strategy, with 85% using it for content creation (Source: [7]).

And yet comprehension barriers are rising: 71.7% of marketers reported comprehension barriers in 2024, up from 41.9% in 2023 (Source: [7]). That’s a quiet warning. Adoption without understanding usually produces busywork, not advantage.

The circle closes back at that 16%. AI-driven search doesn’t pick winners because it’s magical. It picks winners because some teams treat it like a measurable surface—then earn the right to be cited across the places models actually look. Everyone else keeps reporting “SEO traffic” while the buyer gets their answer somewhere else.