AI Search 2026: Key Findings From 300 Enterprise Leaders

TL;DR: The AI search 2026 key findings from 300 enterprise leaders are clear: this is now a top-of-mind priority, not an experiment. Branch found that 98% of enterprise marketing leaders are optimizing for AI search or plan to within a year, 89% saw it improve performance in 2025, and AI search traffic is projected to reach a mean of 50% of website traffic by the end of 2026 (Branch, 2026). The recurring theme underneath the numbers: AI search rewards organizations that can serve up unified, context-rich answers, which is as much an internal retrieval problem as a marketing one.

In 2025, AI search stopped being a rounding error and became a real share of enterprise traffic. To find out what was working, Branch surveyed 300 enterprise marketing, growth, and digital leaders at companies with 500 or more employees (Branch, 2026). This post pulls out the key findings and what they say about where retrieval is heading. The short version: leaders are not waiting, and the hardest problems are about context and measurement, not hype.

What is AI search, and why does 2026 matter?

AI search is discovery that happens inside generative systems like ChatGPT, Perplexity, and Google’s AI Overviews, where a model synthesizes an answer instead of returning a list of links. Generative engine optimization (GEO) is the practice of making a brand show up inside those synthesized answers (Branch, 2026).

2026 matters because adoption crossed a line. Branch reports that 98% of enterprise leaders are either actively optimizing for AI search or plan to within 12 months, and 28% are putting more than half their marketing budget behind it (Branch, 2026). When nearly everyone is in, the question shifts from “should we” to “how well.”

AI search is growing fast, but it is not killing SEO

The common assumption is that AI search will cannibalize Google. The survey data points the other way. Enterprise leaders project traditional SEO traffic will grow from a mean of 45% of website traffic in 2025 to 53% in 2026, while AI search traffic grows from a mean of 35% to 50% over the same window (Branch, 2026).

Both channels expand at once. That has a practical cost: more touchpoints to track and harder attribution. Only 26% of respondents said AI search drove more than half their website traffic in 2025, but 49% expect to cross that threshold by the end of 2026 (Branch, 2026). That is close to a 2x jump in a single year.

Did AI search actually improve performance?

For most teams, yes. Among the 300 leaders surveyed, 89% said AI-powered search and large language model platforms improved their marketing performance in 2025. Of those, 35% saw significant improvement of 10% or more, 54% saw slight improvement, and only 3% reported a decline (Branch, 2026).

The effect spread past SEO teams. Performance marketing (62%), product marketing (59%), CRM and lifecycle marketing (52%), and data and analytics (45%) all reported meaningful impact (Branch, 2026). When a channel touches that many functions, the underlying knowledge has to be consistent across all of them.

Where is the money going?

Results pull investment. Among enterprise leaders surveyed, 65% are dedicating at least 25% of their 2026 marketing budget to AI search optimization, and 28% are committing more than half (Branch, 2026). The bets are uneven by industry: 40% of financial services respondents are putting a majority of their budget behind AI visibility, 2.5x the rate of retail (Branch, 2026).

The top tactics are foundational rather than clever. Leaders named improving crawlability for AI tools (62%), tracking AI-driven traffic and citations (60%), creating LLM-friendly formats like FAQ and Q&A pages (58%), and refreshing existing content for AI summaries (56%) (Branch, 2026). Most of this work is in-house: only 25% are using outside agencies or vendors (Branch, 2026).

AI is moving from discovery to transactions

This is no longer only a discovery story. Among the 300 leaders, 87% expect platforms like ChatGPT, Perplexity, and Google’s AI Overviews to complete closed-loop transactions for their products within the next 12 months, and 91% say agentic AI will have a positive impact on their business overall (Branch, 2026).

Expectations vary by sector. Retail and e-commerce leads at 64%, followed by financial services and health and wellness at 58% each, food and beverage at 44%, media and entertainment at 36%, and travel and hospitality at 24% (Branch, 2026). When an AI agent is closing a sale, it needs accurate, current, connected answers about products, pricing, and policy. Stale or fragmented data becomes a revenue risk, not just an annoyance.

The measurement gap nobody is naming

Here is the finding that should give leaders pause. Two-thirds (66%) say they feel “very” or “extremely” confident measuring AI-driven conversions, and 80% say AI attribution is clearer than traditional SEO (Branch, 2026).

The same group then listed real gaps: 26% cannot track the user journey from AI discovery to conversion, 24% say their analytics tools cannot handle AI attribution, 16% lack referral data from AI platforms, and 14% have no clear attribution model for AI traffic (Branch, 2026). Branch explains the contradiction simply: most teams are measuring basic referral traffic, which is not the same as understanding how AI shaped the full path to purchase. When someone meets a brand in an AI Overview and converts three days later through branded search, the AI touchpoint vanishes from the model.

On risk, leaders skewed cautious: 61% framed AI discovery as a concern versus 39% as an opportunity. Accuracy and transparency tied with data privacy and security as the top worries, each cited by 19% (Branch, 2026). Accuracy worries trace back to a single root cause: when AI systems pull from scattered, unconnected sources, they are more likely to be wrong about your brand.

A concrete example: Vantage Health

Picture Vantage Health, a mid-size health and wellness company. Its team reads the survey and reacts: 58% of health and wellness peers expect agentic AI to drive significant impact, so leadership greenlights an AI search push (Branch, 2026).

Then reality sets in. Product facts live in a PMM workspace, compliance language sits in a separate policy tool, pricing lives in a billing system, and last quarter’s positioning is buried in a thread no one can find. When an AI assistant answers a customer’s question about a Vantage Health plan, it stitches together whatever it can reach, and sometimes it gets the eligibility rules wrong. That is the accuracy and privacy concern the survey flagged, showing up in a real workflow.

What Vantage Health needs is not another content tool. It needs a connective layer so that the same correct, current answer is available to people and to AI agents. This is the job of a semantic layer: a knowledge graph plus AI search that links entities (products, policies, prices, decisions) across systems so one query can traverse them. SemanticOS is built for exactly this, acting as an operational brain that connects fragmented tools so retrieval is unified and context-rich. The external GEO work the survey describes only pays off when the internal answer it surfaces is right.

Key takeaways

• AI search is a settled 2026 priority: 98% of enterprise leaders are optimizing for it or plan to within a year, and 89% saw it improve performance in 2025 (Branch, 2026).
• It complements SEO rather than replacing it; both channels are projected to grow toward roughly half of website traffic each (Branch, 2026).
• Money is following results, led by financial services, and the top tactics are foundational: crawlability, measurement, and LLM-friendly content (Branch, 2026).
• The real gap is between confidence and capability: 66% feel confident measuring AI conversions, yet a quarter cannot track the journey or lack the tools to (Branch, 2026).
• As AI agents start closing transactions, accuracy depends on unified, context-rich retrieval, which a knowledge-graph layer like SemanticOS is designed to provide.