89% of Marketers Say AI Search Worked in 2025 — But Most Can't Prove It

A major new survey of enterprise marketing leaders has confirmed what many in the AI industry have suspected for some time: AI-powered search is no longer an experiment. It is a primary channel. And the organizations that fail to build the measurement infrastructure to support it are already falling behind.

Branch, an AI-powered mobile measurement platform, surveyed 300 enterprise marketing, growth, and digital leaders for its latest report on AI search and discovery. The findings paint a picture of a market that has embraced AI search with remarkable speed — while struggling to answer the most fundamental question in marketing: what is actually working?

The Numbers Are Striking

The headline finding is difficult to ignore. According to the Branch report, 89% of enterprise leaders say AI-powered search and LLM platforms improved their marketing performance in 2025. Platforms including ChatGPT, Perplexity, and Google's AI Overviews are now influencing discovery, driving traffic, and increasingly closing sales.

The pace of budget reallocation reflects this confidence. 65% of enterprise leaders are dedicating at least 25% of their 2026 marketing budgets to AI search optimization. 28% are allocating more than half. And nearly all companies surveyed — 98% — are already optimizing for AI search or planning to within the next year.

The optimization priorities are shifting, too. Rather than the traditional SEO focus on backlinks and keyword density, teams are now concentrating on crawlability for AI systems, AI-friendly content formats, structured data, and the ability to track AI-driven traffic across platforms.

Traditional SEO Is Not Dead — But It Has Company

One of the most significant findings in the report is that AI search is not replacing traditional search. It is growing alongside it — and growing faster.

By the end of 2026, enterprise leaders expect traditional SEO to drive approximately 53% of website traffic on average. AI search, however, is projected to drive around 50% — a figure that would have been considered implausible just two years ago.

The practical implication is that marketing teams can no longer afford to optimize for one channel at the expense of the other. Traditional search and AI search must now be managed simultaneously, requiring a fundamental rethinking of how content is created, structured, and distributed.

The survey also revealed that AI's influence extends beyond SEO teams. Performance marketing, product marketing, CRM and lifecycle marketing, and analytics teams are all experiencing the impact of this shift as discovery increasingly moves to AI platforms.

The Transaction Moment Is Closer Than Expected

Perhaps the most commercially significant finding in the Branch report is the scale of expectation around AI platforms becoming direct transaction channels.

87% of enterprise leaders believe that AI platforms like ChatGPT, Perplexity, and Google AI Overviews will directly close sales within the next 12 months — not merely drive discovery and hand off to traditional purchase pathways.

This represents a structural shift in the commercial role of AI search that most organizations are only beginning to plan for. When a customer can discover, evaluate, and purchase a product without ever leaving an AI interface, the entire funnel architecture that marketing teams have built over the past decade needs reconsideration.

The Measurement Gap Is the Real Story

Despite the confidence in AI search's performance impact, the Branch report identifies a gap between perceived and actual measurement capability that should concern anyone responsible for marketing ROI.

26% of enterprise leaders report they cannot track the user journey from AI discovery to conversion. 24% say their analytics tools are simply not ready for AI attribution. And the challenge runs deeper than tool capability — AI frequently influences conversions indirectly, with users discovering a brand through an AI platform but converting later through traditional search, direct traffic, or another channel entirely. That indirect influence is extraordinarily difficult to capture in standard attribution models.

The result is a market that is outpacing its own infrastructure. Companies are committing significant budget to AI search optimization based on perceived performance improvements — but without the measurement frameworks to validate those investments with precision.

What This Means for Automation-First Organizations

For Hamza Baig, founder of the Automation Institute and creator of Hexona Systems, the Branch report confirms a pattern he has consistently observed across the organizations he works with: the gap between adoption and infrastructure is the most dangerous place a business can be.

"The 89% figure doesn't surprise me at all — AI search has been delivering real results, and the marketers who leaned into it early are seeing that," Baig says. "What does concern me is the measurement gap. You cannot optimize a system you cannot measure. The companies moving fastest right now are the ones building the attribution infrastructure alongside the AI search strategy — not waiting for the tools to catch up later. That is the automation mindset applied to marketing: build the system, measure everything, and iterate. The ones skipping the measurement step are building on sand."

Baig's perspective reflects a broader principle that underpins his work at both the Automation Institute and Hexona Systems: the value of AI adoption is only as durable as the infrastructure built around it. Moving fast without measurement produces results that cannot be replicated, scaled, or defended when budgets come under scrutiny.

Where Enterprise Leaders Are Focusing for 2026

The Branch report identifies the top concerns among enterprise leaders navigating the AI search landscape. Accuracy and transparency of AI outputs lead the list, followed by data privacy and security, and internal readiness and skills gaps.

Notably, measurement and ROI — despite being the most operationally significant challenge — ranked lower among stated concerns than expected. The report suggests this may reflect a combination of optimism about future tool development and a tendency to prioritize channel adoption over attribution sophistication.

The organizations contributing perspectives to the report — including MoEngage, M+C Saatchi Performance, Capacity Interactive, and Yodel Mobile — broadly echoed the finding that cross-channel attribution and AI influence tracking are the most pressing capability gaps for 2026.

The Immediate Priority Is Clear

The Branch report closes with a clear strategic directive: the companies that will succeed in the AI search era will focus on measurement, cross-channel attribution, and tracking AI's influence on conversions — not just traffic.

That means building the infrastructure now, before budget commitments lock teams into optimization strategies that cannot be properly evaluated. It means investing in analytics capabilities alongside investments in content and SEO. And it means treating AI search not as a separate channel to be managed in isolation, but as one layer of a broader, interconnected discovery ecosystem that requires holistic visibility to optimize effectively.

The 89% who reported performance improvements in 2025 are right to feel confident. But confidence without measurement is not a strategy. It is a gamble — and in a market moving this quickly, the cost of getting it wrong compounds fast.