AEO Guide: Maintain Strategic Focus
How To Build a Search Strategy in a System That Keeps Changing
Here’s what we’re learning about AI search and how we’re staying oriented as it evolves.
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Chances are that you’re sitting with the same uncomfortable truth other marketers are feeling right now:
AI search shifts discovery from an observable journey driven by user choice to AI-mediated interactions driven by retrieval and synthesis.
People used to run a search, scan links, choose a source, explore, learn, and then decide what to do next.Â
Now, more and more searches either begin in generative AI experiences, or end with AI Overviews. These search systems compress the consideration process. AI systems retrieve, filter, summarize, and present a ready-made point-of-view without the need for user exploration or even interpretation.
So, how do you reorient to this new normal? How do you make sense of a fragmented customer journey that’s increasingly harder to define?  Â
The Speed of Change Outpaces Planning Cycles
AI search is constantly evolving. Retrieval models are updated through rolling deployments, where changes are introduced often and incrementally rather than in a smaller series of discrete updates. And, even small adjustments can impact how content is interpreted or whether it’s included in generated answers.
Because AI search updates aren’t rolled out on a predictable schedule, they don’t align with traditional planning cycles or pause to accommodate them.
By the time you set a roadmap, the system it was designed for may already be different.
Most search and content strategies assume longer timeframes. Teams set priorities, build roadmaps, and execute them over months. With AI systems evolving mid-cycle, a strategy can quickly become outdated.
That gap isn’t a strategy failure. It’s a mismatch between the speed of change and the speed of planning.
Designing a Strategy for a System in Motion
Once you accept that AI search will keep changing, the question shifts from how to execute a plan within a changing system to how to make good decisions within a system that doesn’t stand still.
An adaptable strategy emphasizes nimble decision-making over the rigid execution of a fixed plan. It supports monitoring search behavior, interpreting what’s changed, and adjusting direction without starting from scratch.
When you build adaptability into your strategy, you create a stable approach to succeeding in an unstable environment.
Don’t Let Change Awareness Create False Urgency
Adaptability matters in a shifting search landscape, but it introduces its own risks. When you’re continually monitoring for new signals, you’re constantly tempted to react to every new piece of data. It can be easy to misinterpret every fluctuation as a decision point.
Retrieval behavior and AI search visibility will fluctuate as AI search models are updated. Some of these shifts will point to real changes in how they interpret content. Without a way to separate the signal from the noise, you may be tempted to act hastily when a wait-and-see approach might be more beneficial.
To operate effectively in a system that won’t sit still, your strategy needs a framework for evaluating change. Without that anchor, every new signal looks equally important.
Understanding Entities as a Diagnostic Lens for Visibility
Because entity understanding tends to change more slowly than rankings, citations, or mentions, it can serve as a valuable point of stability in an otherwise volatile search environment.
In search systems, an entity is a recognized concept, such as a topic, organization, product, or service, that a search system can identify, relate to other concepts, and reason about over time.
Entity recognition describes how search systems determine what a piece of content represents, how that meaning connects to a broader network of related concepts, and why a particular source should be considered a credible authority on a specific topic.
It relies on the consistent association of several related entities.
For example, a topical entity refers to the concept covered in a piece of content. It defines the idea a search system is trying to understand and place within its broader knowledge of the subject area.
Service or solution entities signal what the source (brand or author) does in relation to that topic, clarifying how its offerings or capabilities connect to the subject being addressed.
The brand, author, or source entity represents who is speaking and whether the system should treat that source as credible, experienced, and relevant for answering questions within that subject area.
Building Brand Endurance Through Entity Strength
Search systems don’t evaluate these entities independently. They evaluate how consistently and persistently these entities align over time. They look for repeated patterns that connect the same topic, the same source, and the same kind of expertise across different contexts.
When that consistency holds, the system develops a more durable understanding of how a source should be positioned in relation to a topic. Over time, this influences when the source is cited, how it relates to specific queries, and how reliably it appears, even as the search system evolves.
We call that durable understanding “entity strength”.
Because of their relative stability, entities can function as a diagnostic reference point for interpreting changes in search visibility.
If visibility in AI search systems changes, one of three factors is typically at play.
- The system’s understanding of the topical entity shifted. The content may no longer represent the concept as clearly or as strongly as competing sources.
- The system still understands the topic and the source, but struggles to retrieve the content. The meaning is present, but structure or technical issues make it difficult to extract and summarize for use in an answer.
- The system still understands and can use the content, but surfaces it differently. This can result from rolling updates, changes to summarization behavior, or fluctuations in how sources are mixed and displayed.
Since entity strength shows up in the repeated associations a system makes between a source and a topic across different queries and contexts, it gives you a way to diagnose what kind of change you’re actually seeing.Â
Ask yourself:
- Does the system still associate our source with this topic?
- Are we still grouped alongside the same conceptual peers?
- Do we still appear across the same categories of questions?
- Has another source become more strongly associated with the topical entity?
Entities provide an anchor for interpreting visibility changes and distinguishing durable signal from short-term noise.
How We Maintain a Longitudinal View of Search Changes
Victorious runs ongoing studies that observe how AI search systems behave over time. That includes how content is retrieved, summarized, cited, and surfaced across different queries and generative experiences. We track how entity relationships hold or decay, how retrievability issues emerge, and how system-level changes affect visibility without changes to topical interpretation.Â
Rather than being intended to capture isolated outcomes, this work helps us build a longitudinal view of how AI search behaves, so individual changes can be interpreted in context rather than treated as standalone events.
That context is where the value of our technology research lies.
Most teams experience AI search as a series of disconnected signals. Rankings shift. Citations change. Summaries appear and disappear. Acting on those signals in isolation increases the risk of responding to the wrong problem.
By maintaining a longer view, we can distinguish between changes that reflect a real shift in understanding and changes that reflect normal system behavior expressing the same understanding differently. That distinction allows strategy to stay grounded even as visibility fluctuates.
Our research creates a decision-making framework that helps us interpret search changes before we act on behalf of our customers. Instead of reacting to noise, our decisions are anchored in observed patterns that persist over time. The depth and breadth of our commitment to ongoing research positions us to build durable visibility by staying aligned with how search systems actually behave as they evolve.
Director of Brand Growth
Jill Maldonado helps shape how Victorious approaches growth. Her work spans strategy, storytelling, and creative direction, with an emphasis on how visibility and demand shape brand perception at the system level.
Updated Jan 12, 2026
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