How to withstand algorithm updates and optimize for AI search
The SEO industry is undergoing a profound transformation in 2025. As large language models (LLMs) increasingly power search experiences, success now depends on withstanding traditional algorithm fluctuations and strategically positioning brands within AI knowledge systems. This article explores key insights and practical implementation steps to navigate this evolving landscape.
Understanding Algorithm Updates
The first step is drilling down to understand what changed after an update. Using filtering and segmentation, you can pinpoint issues with precision. For example, you might discover that a traffic drop:
One of the most powerful approaches to algorithm analysis is using time series forecasting to establish a baseline of expected performance. Meta’s Prophet algorithm is particularly effective for this purpose, as it can account for:
By establishing what your traffic “should” look like based on historical patterns, you can clearly identify when algorithm updates cause deviations from expected performance. The key metric here is the difference between actual and forecasted values. By calculating these deviations and correlating them with Google’s update timeline, you can quantify the impact of specific updates and distinguish true algorithm effects from normal fluctuations.
Brand AI visibility tracking
Here, I’ll illustrate the approach to measuring and interpreting AI visibility for one participating brand. We utilize two prompt modes and collect this data on a daily basis:
This bidirectional analysis creates a structured approach to AI model brand perception. The analysis performed after two weeks of data collection revealed that this brand is strongly associated with:
This shows strong alignment with their core business. However, when looking at which brands AI models associate with their key product categories, dominant players like Nike, Adidas, and Under Armour consistently outrank them.
Tracking User Intent
As search engines’ understanding of user intent evolves, tracking intent shifts becomes crucial. By analyzing how Google categorizes and responds to queries over time, you can identify when the search engine’s perception of user intent changes for your target keywords. This approach involves:
When you notice declining visibility despite stable rankings, intent shifts are often the culprit. The search engine hasn’t necessarily penalized your content. It’s simply changed its understanding of what users want when they search those terms.
Remaining Competitive
A particularly valuable insight comes from comparing “grounded” responses (influenced by current search results) with “ungrounded” responses (from the model’s internal knowledge). This comparison reveals gaps between current online visibility and the AI’s inherent understanding. Ungrounded responses show stronger associations with cycling and esports jerseys, while grounded responses emphasize general custom sportswear. This highlights potential areas where their online content might be misaligned with their desired positioning.
A particularly valuable insight comes from comparing “grounded” responses (influenced by current search results) with “ungrounded” responses (from the model’s internal knowledge). This comparison reveals gaps between current online visibility and the AI’s inherent understanding. Ungrounded responses show stronger associations with cycling and esports jerseys, while grounded responses emphasize general custom sportswear. This highlights potential areas where their online content might be misaligned with their desired positioning.
Optimizing for AI Models
While traditional algorithm analysis remains important, a new frontier has emerged: optimizing for representation within AI models themselves. This shift from ranking pages to influencing AI responses requires entirely new measurement and optimization approaches.
Traditional rank tracking tools don’t measure how your brand is represented within AI models. To fill this gap, we’ve developed AI Rank, a free tool that directly probes LLMs to understand brand associations and positioning.
Strategic Adaptations
Based on these insights, here’s how forward-thinking brands can adapt to the AI-driven search landscape.
LLMs understand the world through entities and their relationships. To strengthen your brand’s position:
Implement continuous measurement of your brand’s representation in AI systems. The shift from traditional search to AI-driven information discovery requires a fundamental strategic revision. By combining these approaches, brands can thrive in both current and emerging search paradigms. The future belongs to those who understand how to shape AI responses, not just how to rank pages.
Savvy data scientists will notice that some data tidying is in order, starting with normalizing terms by removing capitalization and various artifacts. In the coming weeks, we’ll also work on better concept merging/canonicalization, which can further reduce noise and perhaps even add a named entity recognition model to aid the process. Overall, we feel that much more can be derived from the collected raw data and invite anyone with ideas to contribute to the conversation.
