At Adobe Summit, Semrush unveiled a new Brand Visibility operating model designed to help enterprises navigate a rapidly shifting discovery landscape shaped by AI search, autonomous agents, and fragmented digital touchpoints.

The way brands are discovered online is undergoing a structural transformation. Traditional keyword-based search—long the foundation of digital marketing—is giving way to AI-driven discovery systems that surface answers, not links. In response, Semrush has introduced a new framework aimed at redefining how organizations approach visibility in this environment.

The company’s Brand Visibility framework positions discoverability as a measurable, orchestrated outcome rather than a byproduct of channel execution. It defines brand visibility as the extent to which a company is discoverable, accurately represented, and commercially actionable across both human-driven and machine-mediated environments.

At the center of this model is a new concept: Agentic Search Optimization (ASO). Unlike traditional SEO, which focuses on ranking web pages, ASO is designed to ensure that brands are recognized, interpreted, and selected by AI systems—including chatbots and autonomous agents—as they evaluate information and generate responses.

This shift reflects a broader change in user behavior. According to Gartner, traditional search volume is expected to decline by 25% by 2026, as users increasingly rely on AI-generated answers from platforms like ChatGPT and Google Gemini. In this context, visibility is no longer about appearing on a results page—it is about being embedded within the answer itself.

Semrush’s research highlights a critical challenge for enterprises: the “alignment gap.” While many organizations have invested heavily in digital marketing, few have adapted their operating models to account for AI-driven discovery. The result is fragmented execution across SEO, content, and AI initiatives.

The data underscores the issue. Only 22.6% of organizations have a unified process for managing content across traditional search and AI environments. Meanwhile, more than half of enterprise teams report being only partially aligned—or entirely siloed—when it comes to brand visibility strategy.

This lack of alignment has measurable consequences. Fully aligned teams are significantly more likely to report that their visibility efforts are actionable and measurable, while disconnected teams struggle to quantify performance in AI-driven channels.

To address this, Semrush is proposing a structured operating model built around orchestration rather than execution. The framework introduces a four-stage lifecycle: foundation, content, distribution, and feedback. Together, these stages create a continuous loop in which brand narratives are defined, deployed across channels, and refined based on performance signals.

A key element of this approach is the concept of a unified content supply chain. Instead of creating separate strategies for SEO, social media, and AI platforms, organizations define topics and messaging once and distribute them across all discovery surfaces. This consistency is critical for building authority in AI systems, which rely on patterns and signals across multiple sources to determine relevance.

The framework also introduces a new organizational role: the Brand Visibility Orchestrator. This role is designed to bridge the gap between strategy and execution, ensuring that brand narratives remain consistent across channels and that performance data is fed back into decision-making processes.

This reflects a broader trend in enterprise marketing. As the number of channels and platforms increases, coordination becomes more complex. Companies such as Adobe and Salesforce have already begun integrating AI-driven insights into their marketing clouds, emphasizing the need for unified data and workflows.

Semrush’s approach extends this idea into the realm of discovery itself. By treating visibility as a system-level outcome, the company is encouraging organizations to rethink how they measure success. Metrics such as share of voice, AI citations, and sentiment within generated responses are becoming as important as traditional rankings and traffic.

Early results suggest the potential impact of this shift. Semrush reports that it was able to nearly triple its own AI share of voice—from 13% to 32%—within a month by applying the principles outlined in its framework. While internal benchmarks should be interpreted cautiously, they highlight the potential gains from coordinated execution.

From a market perspective, the introduction of a formal operating model signals a maturation of AI-driven marketing strategies. According to IDC, organizations that successfully integrate AI into their marketing operations are more likely to achieve measurable improvements in efficiency and customer engagement.

However, implementing such a model is not without challenges. It requires changes in organizational structure, investment in new tools, and a shift in mindset from channel-specific optimization to system-wide orchestration. For many enterprises, this represents a significant transformation.

Still, the direction is clear. As AI systems become the primary interface for information discovery, brands must adapt to a world where visibility is determined not just by algorithms, but by how effectively they communicate across interconnected platforms.

Semrush’s Brand Visibility framework is an attempt to provide a roadmap for that transition—one that aligns strategy, technology, and execution in an increasingly complex digital ecosystem.

Market Landscape

The shift toward AI-driven discovery is reshaping the marketing technology landscape. Gartner forecasts a decline in traditional search, while IDC emphasizes the growing importance of AI in customer engagement and marketing operations.

Technology leaders such as Google, Microsoft, and Amazon are investing heavily in AI-driven discovery systems. Meanwhile, platforms like Adobe and Salesforce are embedding AI into marketing workflows, increasing the need for unified visibility strategies.

Top Insights

• Semrush introduces a Brand Visibility framework that shifts marketing from channel-based execution to orchestrated discovery across AI and traditional search environments.
• Agentic Search Optimization (ASO) emerges as a new discipline focused on ensuring brands are selected and represented within AI-generated answers and autonomous systems.
• Research reveals significant alignment gaps in enterprise marketing teams, with most organizations lacking unified processes for managing visibility across search and AI channels.
• The framework’s lifecycle and new “Brand Visibility Orchestrator” role reflect a broader move toward integrated, system-level marketing operations in the AI era.

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