Moving fast, flying blind:
2026 AI search survey

Our survey surfaced a split personality.

Marketing and PR professionals sound sure of themselves on AI search—until you ask them how it works and what they’re doing.

Under the surface, confusion and anxiety are readily apparent, especially lower on the corporate ladder.

Marketers agree AI search is the future:

Many marketing and PR professionals appear stuck on the first step of AI search optimization: monitoring.

That’s the foundation of AI search, not the finish line. Even still, too many teams are only seeing part of the picture.

While 45% are testing their brand’s or clients’ visibility across multiple AI platforms, most aren’t doing anything with the data they’re gathering—58% aren’t updating existing content to improve AI citation likelihood and 63% aren’t mapping content, press materials, or campaigns to real audience prompts.

Most teams are only scratching the surface:

• 60% aren’t analyzing which media sources are surfaced by AI systems.
• 67% aren’t analyzing AI bot traffic.
• 70% aren’t monitoring sentiment toward their brand in AI answers.
• 71% aren’t analyzing AI share of voice against competitors.

What’s more, of the teams actively tracking their AI search performance, 37% are using AI platforms to manually check brand mentions (e.g., going into ChatGPT to see how they show up for certain prompts).

The fact that only about one-third of our respondents are analyzing AI bot traffic is an issue worth highlighting. It’s not a minor gap; it’s a missed signal.

When AI user agents retrieve content from your site, it means real people are prompting about you or your category. That’s a prospective buyer, mid-research—and it’s something most teams are ignoring.

For a quarter century, digital marketing has been dominated by SEO tactics. But AI search is a different animal. Or vehicle, if you will.

The team at Scribewise likes to say that SEO is like a spark plug—without it, the car won’t go. But you can’t drive a spark plug to your destination. Our data shows that many marketers are operating with an SEO mindset for AI search, and likely getting frustrated that the spark plug isn’t moving fast enough.

Some teams see content volume as the solution (i.e., give web crawlers more content, rank better in search engine results pages, show up better in AI answers). Today that means using AI to create content faster.

However, studies have shown that while scaling AI-generated content can lead to rapid improvements in traditional and AI search performance, the falloff comes quickly and leaves brands worse off than before.

Still, that’s the path many are taking. In the process, they’re skipping over content already on their site that can be optimized via AI accessibility, technical hygiene, and LLM-friendly content improvements.

When asked to describe their organization’s primary approach to AI visibility content, 21% say “Publishing a high volume of new content to increase the likelihood of AI visibility” and 21% say “Producing AI-assisted content at scale to maintain broad topic coverage.”

AI-generated or not, 42% of respondents are indexing on scaled content as their fix to AI search optimization.

72% aren’t concentrating on developing a targeted content strategy based on how specific audiences prompt AI tools, and 77% aren’t focused on refining and optimizing existing content to improve how AI systems interpret and cite it.

When it comes to the updates teams are implementing to make their content more visible to AI, 45% are employing tactics like writing content to prioritize direct answers (e.g., FAQ formats, answer-first writing, summary sections).

And in terms of offsite content, 46% are ensuring brand information is present and accurate on authoritative third-party sources (Wikipedia, industry databases, review platforms).

But 61% aren’t working to secure brand mentions in trusted media sources.

The Navy SEALs have a saying: “Slow is smooth, and smooth is fast.” If the goal is sustainable and compounding improvement, putting the work in to optimize content for AI is more important than simply generating content for AI.

The volume trap is especially costly in B2B professional services, where credibility is the primary currency—large volumes of undifferentiated or just flat-out bad content undermine the perception of expertise you’re looking to build.

Misalignment is a direct tax on execution, and it’s a tax lots of companies are paying.

Many departments outside of marketing view answer engine optimization/generative engine optimization (AEO/GEO) as SEO 2.0. The thinking goes that if you rank well in Google, you’ll show up prominently in AI answers.

SEO still matters. It has a direct impact on AI responses. But it’s not one-to-one. In fact, studies have pegged the overlap between Google’s top-10 organic results and AI Overview citations at just 17–38%.

Meanwhile, 76% agreed that more input from sales teams on customer questions and objections would improve AI visibility strategy, and 81% say more collaboration from technical teams, such as web development, would improve their AI visibility strategy.

On the agency side, 71% of respondents who offer GEO as a service say clients don’t understand how the rise of AI search impacts their brand’s visibility:

Ranking in traditional search is not the same thing as being the answer in AI search. And you can’t measure probabilistic AI answers the same way you would more deterministic SERPs.

The fix starts with agreeing on what good looks like so everyone’s grading the same test.

Most teams are investing in technology for AI search optimization: 73% currently use tools to monitor brand or client visibility in AI answers.

But many of these tools are not purpose-built for AI search.

AI search monitoring has become commoditized to a large degree. Many vendors now offer AI visibility dashboards.

The hard part comes after: Turning that data into clear actions. That’s where teams get stuck, and it’s reflected in the numbers above.

Still, it’s worth pointing out that the teams using a dedicated AI search tool outperformed on 25 of the 26 tactics we measured for this report.

While teams are swinging between self-assurance and insecurity, 87% agree that AI search has increased pressure to build technical skills alongside editorial and strategic capabilities.

But 58% say their training/education is not keeping pace with new AI visibility expectations.

Only 6% are not planning any action around AI search visibility in the next 12 months. The awareness is near-universal; the gap is in capability.

Organizations that treat the solution as a deliberate build rather than a slapdash fix stand a better chance of turning confidence into competence.

And for agencies, the 33% of in-house teams planning to bring in outside support represents a significant and near-term opportunity.