Understanding LLM Perception Drift in AI Marketing

As technology continues to evolve, the integration of large language models (LLMs) such as ChatGPT, Gemini, and Claude in various sectors has become increasingly prominent. From search engines to content generation, these AI systems have transformed how brands are perceived and how potential buyers make decisions. A recent survey indicated that 80% of tech buyers now leverage generative AI alongside traditional search methods to research vendors, highlighting a significant shift in buyer behavior.

What Is LLM Perception Drift?

LLM perception drift refers to the changes in how AI models reference and rank brands within specific categories over time. This dynamic has crucial implications for B2B buyers and marketers, offering insights into brand visibility and authority. Previsible, a company researching this phenomenon, has introduced the concept of LLM perception drift to measure changes in brand representation. Their studies are shedding light on how AI affects brand awareness and preference, crucial in the context of SEO and online marketing.

Shifts in Brand Visibility: A Case Study

In a focused study of project management software conducted by Previsible using Evertune’s data, the researchers compared brand visibility from September to October 2025. The findings suggested significant fluctuations in how various brands were perceived by AI systems. For instance, well-known brands such as Atlassian showed a strong upward trend, while others like Slack and Trello experienced substantial declines in their AI recognition scores.

Decoding Brand Score Fluctuations

Understanding the reasons behind brand score changes is pivotal for marketers. Evertune’s AI brand score measures how frequently a brand is recommended by LLMs without being specifically mentioned. This score encompasses visibility and rank position, indicating how well a brand is recognized. In the project management space, notable shifts were observed: Atlassian’s score surged by +5.50, while Slack dropped by -8.10, reflecting a notable alteration in AI’s perception of these brands.

The Dynamics of Category Entanglement

An intriguing aspect of these shifts is what is known as category entanglement. This phenomenon occurs when LLMs begin combining different types of tools and brands into broader conceptual categories. Instead of being narrowly defined, brands like Atlassian are now associated with terms like operations and enterprise productivity, bringing them into a wider sphere of recognition. This trend underscores the need for brands to adapt their positioning strategies to maintain relevance in this evolving landscape.

The Role of Ecosystem Advantage

Another critical factor in these shifts is the ecosystem advantage. Brands with a suite of interconnected products, such as Atlassian, benefit from higher visibility scores due to their comprehensive offerings and integrated services. For instance, Microsoft’s positive movement in scores (+2.08) can be attributed to its broad product ecosystem that resonates well within the AI’s referencing frameworks.

Emerging Brands and Their Rising Signals

The fluctuations in brand perception are not limited to established giants. Emerging brands like Celoxis (+5.17) and TeamGantt (+0.92) are finding their place within AI responses, demonstrating that smaller players can gain visibility without having a dominant presence in traditional SEO landscapes. This trend is a crucial opportunity for B2B brands to optimize their strategies in AI-driven environments effectively.

The Importance of AI Brand Signal Stability

As we move into 2026, AI brand signal stability is becoming a new key performance indicator (KPI) for measuring online visibility. This metric reflects the consistency of a brand’s perception across LLM outputs over time. Brands exhibiting stable scores indicate strong semantic relevance, meaning they are recognized consistently within their category. Conversely, brands with volatile scores may struggle to maintain visibility and relevance, especially as competitive content expands.

Tracking Change Across Various Business Verticals

The dynamics of LLM perception drift are not unique to project management; similar patterns are seen across various business sectors, including CRM, HR technology, and cybersecurity. As AI models recalibrate brand categorizations, the implications for buying journeys can be profound. A minor alteration in brand attention from an AI model can significantly influence which brands are highlighted to potential customers.

Reimagining SEO Strategies in an AI-Driven World

The evolution of SEO to include AI brand visibility metrics moves us towards a focus on optimizing for model memory rather than search indices alone. Brands now must measure their presence within AI ecosystems, track representation, and ensure contextual relevance as LLMs continue to evolve. This shift from How do we rank higher? to How do we ensure AI represents us accurately? is critical for future marketing strategies.

Preparing for the Future: AI Memory as a Marketing Battlefield

As brands attempt to navigate the complexities of LLM perception drift, it’s essential to recognize the growing importance of AI memory in decision-making processes. Companies should focus on how they are represented in AI-generated content, including summaries and comparisons. Much like traditional SEO strategies, brands will need to deploy comprehensive analytics and optimization techniques to remain competitive in an increasingly AI-dominated research landscape.

A Final Thought on AI and Brand Perception

“The shift to AI-driven interactions is not just a trend; it’s the next evolution in how brands must understand and connect with their audience,” says Dr. Elaine Ford, a leading expert in AI marketing.

In conclusion, LLM perception drift is transforming the marketing landscape. Companies that proactively adapt their approach to AI visibility will leverage a competitive edge as the technology continues to reshape how consumers discover and engage with brands.