Why LLM perception drift will be 2026’s key SEO metric

LLMs like ChatGPT, Gemini, and Claude now sit across search, content generation, and recommendations.  

Now, 80% of tech buyers rely on generative AI at least as much as traditional search to research vendors, according to a Responsive survey of B2B buyers. 

This effective transfer of trust in AI discovery has become an enablement tool for B2B buyers, quietly deciding which brands get remembered and which get ignored. 

And those decisions, once invisible, are now measurable.

Previsible has been studying this shift through a new lens called LLM perception drift, the month-over-month change in how AI models reference and position brands inside a given category. (Disclosure: I’m the CEO and co-founder of Previsible.) 

Using recent data from Evertune, which tracks brand visibility within model outputs, we focused on a single case study: the project management software space, comparing September 2025 to October 2025.

The results show how rapidly AI brand perception is evolving, and why that volatility is about to become the next major SEO metric.

Key insights

• LLM perception drift is solidifying as a new visibility metric for SEO and B2B marketing.
• Project management and adjacent enterprise brands saw meaningful movement, with tools like Atlassian surging while Trello, Slack, and Monday.com posted notable drops, according to recent data from Evertune.
• These movements reveal that AI brand perception is dynamic and measurable, reshaping how marketers understand authority and semantic relevance inside large language models.
• In 2026, AI brand signal stability will become central to maintaining digital relevance as LLMs evolve and retraining cycles accelerate.

A subtle shake-up inside the AI mind

Evertune’s AI brand score tracks how likely a large language model is to recommend a brand without being prompted by name.

The score captures two things: how often the brand appears in AI responses (visibility) and where it ranks when it does (average position).

Between September and October, project management brands saw big swings in their scores, signaling real shifts in the AI’s internal brand landscape.

Some of the more striking movements:

• Slack saw one of the most dramatic drops (-8.10).
• Trello fell sharply (-5.59).
• Monday.com (-0.78) and ClickUp (-0.74) also declined.

Meanwhile, gains concentrated around ecosystem and enterprise-connected brands:

• Atlassian jumped (+5.50).
• Microsoft (+2.08).
• Google (+3.62).
• Professional services firms like Deloitte (+5.00), KPMG (+4.00), PwC (+2.45), and EY (+2.75) also climbed.

At face value, it looks like a leaderboard reshuffle. 

But beneath the surface, the shift reflects something deeper – a measurable change in the AI’s unaided brand awareness, a drift in how the model perceives and prioritizes brands, even when nothing visible changed in the market itself.

The meaning behind the drift

The data suggests two overlapping forces driving the change.

Category entanglement

Rather than declining, the category is becoming blurrier.

LLMs are increasingly pulling project management tools into broader conceptual neighborhoods: 

• Operations.
• Digital transformation.
• Workflow orchestration.
• Enterprise productivity.
• IT consulting.

That’s why names like Deloitte, KPMG, and Amazon rise alongside Smartsheet and Atlassian.

Ecosystem advantage

Multi-product ecosystems are gaining attention more reliably.

Atlassian’s +5.50 lift is a prime example: strong documentation, cross-product integrations, and high contextual density drive richer model associations.

Similarly, Microsoft, Google, Amazon, and Adobe all saw upward movement.

Models favor brands that live across multiple contexts. This is the same pattern entity-based SEO taught us, but happening faster and with more volatility.

The more interconnected the brand, the more persistently it appears in AI-generated discourse.

Dig deeper: Alignment for LLM visibility is incredibly complex, but doable

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New entrants, new patterns

The long tail continues to reveal emerging signals.

Brands like Celoxis (+5.17), Workfront (+2.38), TeamGantt (+0.92), LiquidPlanner (+1.40), Podio (+1.65), and GanttProject (+0.45) grew during the period.

This reflects how LLM fine-tuning and retrieval-augmented systems pull in broader datasets: 

• SaaS directories.
• GitHub repositories.
• Technical documentation.
• Reviews.
• Community content.

For smaller B2B brands, this is a key unlock: you can surface in model responses without dominating classic SEO.

Why this shift matters for B2B discovery – and why it’s speeding up

Traditional SEO metrics measure what search engines decide to display. But LLMs don’t index, they synthesize.

That means brand memory inside AI systems is built from associations, context, and semantic density – it goes beyond authority signals or link structures.

The data shows these associations can swing several points in a single month, even for established brands.

That volatility is LLM perception drift, the difference between being surfaced consistently in model outputs versus quietly disappearing from unaided recall.

Dig deeper: Why AI availability is the new battleground for brands

A new AI optimization KPI: AI brand signal stability

In our work with B2B clients, we’re increasingly tracking AI brand signal stability, the consistency of a brand’s presence and positioning across LLM outputs over time.

If your score fluctuates sharply, the model’s understanding is fragile, influenced by retraining cycles, data sparsity, or competitive content expansion.

If it remains stable, you have strong semantic anchoring: the model “knows” you belong to the category.

By 2026, AI brand signal stability will sit next to share of voice and keyword rankings as a core visibility metric.

From project management to every B2B vertical

What’s happening in project management is also happening across different verticals – from CRM to HR tech, analytics, cybersecurity, and every other B2B category.

LLMs are constantly recalibrating which brands belong in which contexts.

As models reinterpret category boundaries, they also reshape buying journeys. 

A small dip or surge in model attention can shift which brands appear in summaries, comparisons, and decision-support workflows.

What looks like a few points of movement today is a glimpse into the next marketing battlefield: AI memory.

Dig deeper: LLM perception match: The hurdle before fanout and why it matters

The next frontier of optimization

This shift represents the natural evolution of SEO, moving from optimizing for search indices to optimizing for model memory. 

The focus is increasingly on:

• Measuring and influencing how brands exist inside AI ecosystems.
• Tracking their representation.
• Reinforcing their associations.
• Ensuring they remain contextually relevant as models retrain.

We’re moving from “How do we rank higher?” to “How do we make sure AI responds correctly?”

That requires new tools, new data pipelines, and a mindset shift: treating LLMs as dynamic perception systems, not static endpoints.

Evertune’s latest dataset reveals something bigger than the month-to-month shifts of Asana, Trello, or Monday.com. 

It captures the early signs of how quickly an AI system’s sense of a category can change. 

These shifts are now visible enough to track, steady enough to analyze, and influential enough that marketing teams will soon watch them as closely as any core marketing metric.

By 2026, a brand’s presence inside AI-generated summaries and comparisons will shape decision-making more than pageviews or clicks ever did. 

Companies that track how they show up in these model-driven moments – and learn to strengthen those signals – will gain a real edge as AI becomes the primary layer of digital research.

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