What Marketing Teams Must Do Differently in 2026

Key findings

Why do marketing teams need to change for AI search?

For two decades, search shaped marketing budgets, team structures, KPIs, and technology. Discovery now depends on systems that interpret, refresh, and reweight information in real time. Leaders face three new questions: who owns local truth, how ready the organization is to operate continuously, and where investment should shift.

The new questions leaders must answer: governance, readiness, and investment.

AI-mediated discovery is reshaping not only how brands are found, but fundamentally how marketing organizations must operate. For two decades, search was the undisputed center of gravity for all digital marketing, influencing everything from budget allocation (SEM, SEO) and team structures (SEO specialists) to customer funnels, key performance indicators (rankings, clicks, conversion rates), customer journey mapping, and technology infrastructure.

Discovery is now shaped by systems that continuously interpret, refresh, and reweight information based on real-time intent. As a result, marketing leaders face a new set of questions that require a profound evolution of the marketing role and organization. Who owns local truth? How ready is the organization to operate continuously? And where should investment shift to reflect how discovery actually works?

60.5% of marketing leaders have created a new role or team dedicated to AI optimization.

The era of AI-mediated discovery demands a complete restructuring of teams and responsibilities. The function must now involve a broader array of people (digital marketers, content creators, social media specialists, and dedicated AI search specialists), leading to the creation of new roles (like the GEO Specialist) and even entirely new teams. This shift necessitates new skill sets centered on workflow management, cross-functional orchestration, and agentic AI. Budgets are already shifting dramatically, with 40% of respondents reporting they invest more than 50% of their marketing budget in generative engine optimization (GEO). New metrics (Share of Voice, Inclusion Rate, citations) are replacing old ones, creating a need for new, integrated tools, like Uberall and the GEO Studio, to manage local complexity at scale.

Governance: who owns local truth in a multi-location brand?

Central teams should set standards for data, content, and measurement, and local teams should execute within those guardrails. Without one structure across marketing, customer service, content, social media, and SEO teams, conflicting signals spread across locations and AI struggles to form a confident view of the brand.

As AI aggregates signals from listings, websites, reviews, and third-party platforms, clear governance becomes a prerequisite for visibility. When ownership is unclear, inconsistency follows, and AI systems struggle to form a confident view of the brand.

Effective teams operate with a clear governance model. Central teams define standards for data, content, and measurement, while local teams execute within guardrails that preserve relevance. The core challenge often revolves around collaboration. AI search performance relies on signals from multiple teams, including marketing, customer services, content creation, social media, and SEO specialists. Without a unified structure, conflicting signals proliferate across locations and platforms. This is critical because AI models reward consistency and precision, making the need for an all-in-one platform crucial to streamline cross-functional collaboration.

This challenge is already visible. In our B2B survey, 61.5% of marketing leaders describe managing local digital presence as very or extremely complex, and 47.5% say four or more teams influence local visibility. In an AI-mediated ecosystem, unclear governance doesn't just slow execution; it directly affects how often and how consistently a brand is surfaced.

Readiness: can the organization operate always on?

Readiness means operating AI search continuously. Models keep updating sources, reinterpreting signals, and rebalancing recommendations, so brands that treat it as a one-time project lose ground. The biggest barriers are structural: fragmented systems and data, and the work of keeping business information accurate across a growing set of platforms and AI-driven experiences.

Visibility within AI-generated answers is inherently dynamic. Models continuously update sources, reinterpret signals, and rebalance recommendations. Brands that treat AI search as a one-time optimization quickly lose ground. Those that adopt continuous monitoring, iteration, and optimization steadily expand their presence.

Readiness, therefore, is not just strategic, it is operational. Yet for many organizations, the biggest barriers are structural. In our survey, 25% of marketing leaders cite fragmented systems and data sources as the largest capability gap preventing success in AI-led search.

This fragmentation shows up most clearly in day-to-day execution. Asked for their biggest pain point regarding AI search, 37% of marketing leaders name keeping business information accurate across an expanding set of platforms. Asked what creates the most operational friction in managing local performance at scale, 49% cite ensuring accuracy across AI-driven experiences and 39.5% updating business information across multiple platforms. As AI systems increasingly pull from distributed and real-time data sources, even small inconsistencies can immediately impact visibility at the moment of intent.

While experimentation with AI tools is widespread, execution at scale remains the gap. 99% of respondents rate an AI orchestration layer, one that can monitor performance, diagnose issues, and automatically execute local optimizations, as very or extremely valuable. This signals a shift in expectations: teams are no longer just looking for AI-powered insights, but for AI-powered operations.

AI also needs to move beyond experimentation. Increasingly, AI agents act as operators, managing updates, generating responses, and supporting optimization at scale. When integrated into core workflows, these agents reduce manual effort and improve responsiveness. When treated as isolated tools, their impact remains limited. As Ana Martinez, Chief Technology Officer, Uberall, explains:

“Agentic AI goes beyond automation. These systems can reason, plan, and act independently toward defined goals and outcomes.”

Looking ahead, marketers are prioritizing local content capabilities as a core pillar of readiness. The most important capabilities for 2026 include scaling localized content dynamically, automating content creation, and benchmarking content performance against competitors, reinforcing the need for systems that can operationalize content, data, and optimization continuously.

Investment: are marketing budgets shifting toward AI search?

Yes, in how the money is split. 83.5% of the 200 marketing leaders surveyed are adapting how they allocate their marketing budget in response to AI search. The playbook sees spend moving from campaign-based media toward content, data, and optimization, with some teams opening new budget lines for data acquisition, preparation, and management.

The final question is whether investment matches the scale of change. As awareness of AI’s role in discovery grows, 83.5% of marketing leaders say they are adapting how they allocate their marketing budget in response to AI search. The shift is about spending differently, reallocating resources from campaign-based media toward content, data, and optimization.

Where budgets are evolving, the pattern is clear. One marketing leader explained:

“We have reallocated some of our traditional advertising budget to AI search-related channels to test their actual impact on improving high-intent traffic and conversion efficiency.”

This shift is also reshaping how organizations structure spend. Some teams have created new budget lines dedicated to data acquisition, preparation, and management, recognizing that AI visibility depends as much on data infrastructure as on marketing execution.

Measurement: how do marketing teams prove the impact of AI search?

Tracking is widespread: most teams track their share of voice in AI chatbot answers, and many treat it as a primary KPI. The harder part is attribution, crediting traffic, revenue, or conversions to AI-driven discovery. The playbook pairs classic SEO KPIs with GEO KPIs such as brand mentions, daily citations, and share of voice.

Unlike earlier digital shifts, most teams are already tracking AI visibility. In our survey, 94.5% of marketing leaders say they track share of voice in AI chatbot answers, and 68.5% treat it as a primary KPI.

The challenge has moved beyond measurement itself to attribution and ROI. More than 30% of respondents cite a lack of clear attribution as a major pain point, making it difficult to determine how much traffic, revenue, or conversion can be credited to AI-driven discovery. As a result, many teams struggle to prove the business impact of AI search to leadership, slowing investment decisions and organizational buy-in.

In 2026, the most effective marketing teams will be those that align investment with how discovery actually works. They will establish clear governance over local truth, build operational readiness for continuous visibility, and measure success using AI-native indicators such as Share of Voice and brand mention frequency. Those that do will turn AI-mediated discovery into a durable advantage. Those who do not will find their visibility shaped by systems they are not equipped to manage.

How was this chapter researched?

The survey results in this chapter come from Uberall’s B2B survey of 200 senior-level US marketers, fielded in the US from December 12 to 31, 2025. It is one of the two commissioned surveys behind the GEO Playbook, alongside a survey of 2,000 consumers in the US, UK, France, and Germany.

Frequently asked questions

Which local content capabilities are marketers prioritizing?

The chapter names three: scaling localized content dynamically, automating content creation, and benchmarking content performance against competitors. Marketers treat local content as a core part of readiness, which calls for systems that run content, data, and optimization continuously.

What role do AI agents play in local marketing operations?

AI agents increasingly act as operators: they manage updates, generate responses, and support optimization at scale. Built into core workflows, they reduce manual effort and improve responsiveness. Uberall CTO Ana Martinez describes agentic AI as systems that can reason, plan, and act independently toward defined goals and outcomes.

What do marketing leaders want from an AI orchestration layer?

99% of respondents rate an AI orchestration layer that monitors performance, diagnoses issues, and automatically executes local optimizations as very or extremely valuable. Execution at scale is where teams fall short, so they now expect AI-powered operations as well as AI-powered insights.

What else does the GEO Playbook cover?

The main playbook covers how AI is changing local discovery, how personal AI agents collapse the consumer journey into a single high-intent moment, why brands go missing or get misrepresented in AI answers, and the three-pillar strategy it recommends for multi-location brands.

Read the full GEO Playbook