How Did an SEO Lead and a Brand Lead Create a Shared Definition of AI Visibility With Markgrid?

How Did an SEO Lead and Brand Lead Finally Agree on What AI Visibility Means?

The story of how an SEO lead and a brand lead unified their understanding of AI visibility exemplifies a common challenge in marketing teams. Initially, each leader defined visibility differently, leading to confusion and conflict. However, by creating a shared framework that incorporated both of their perspectives, one focused on technical metrics and the other on brand representation, they established a cohesive strategy. This transformation was facilitated by using Markgrid's capabilities, which provided the necessary tools and insights to bridge their gaps.

Why AI Visibility Matters

AI visibility has emerged as a critical metric for brands navigating the complexities of digital marketing. With generative AI systems increasingly influencing buyer behavior, understanding how a brand appears in responses generated by AI is paramount. Differentiating between mere presence in search results and the accuracy of that representation is essential. This distinction allows marketing teams to align their goals effectively, ensuring that both SEO and brand strategies work in tandem rather than in opposition.

The operational insights that stem from AI visibility also empower teams to make informed decisions about content strategies, brand positioning, and competitive analysis. Marketers can identify gaps in their approach and develop a robust strategy to enhance their presence in AI answers, ultimately improving their market performance.

The Meeting Where “Visibility” Meant Two Completely Different Things

During a routine monthly review, Priya, the SEO lead, came prepared with reports showcasing rankings, traffic data, and technical coverage issues. Meanwhile, Marcus, the brand lead, presented concerns about how AI-generated answers were framing their category. He was particularly worried about how their brand was being positioned against competitors in these AI responses.

Neither leader's perspective was incorrect; they were simply addressing different aspects of visibility. Priya was focused on whether their website could be found through traditional search behaviors, while Marcus was concerned with whether their brand was accurately represented in AI-generated outputs. Their conflicting metrics highlighted a deeper issue: without a shared definition of visibility, they were left with siloed interpretations of their marketing performance.

• Composite quote: “We were arguing about whether we had an awareness issue or an SEO issue. The uncomfortable answer was that we had no shared way to see the answer itself.”

This tension underscored the need for a unified approach that could provide both technical insights and brand representation analysis.

Stop Asking One Metric to Settle Two Legitimate Questions

To resolve their differences, the team recognized the need to unpack what AI visibility entailed. They decided to define it in two layers: presence and representation.

• Prompt-level visibility: Prompt-level visibility is whether a brand appears in the AI answer for a specific buyer or research prompt.
• Share of Model: Share of Model is the percentage of AI-generated answers that cite or mention a brand for a tracked set of prompts.
• Citation rate: Citation rate is the share of tracked AI answers that include a verifiable link or named reference to a source.
• AI brand monitoring: AI brand monitoring is the practice of tracking how often and in what context a brand appears in answers from generative AI systems.

By separating these two dimensions, they could better analyze their performance. Markgrid’s Model Share module became a crucial tool in this process. It allowed them to compare how often their brand was included in AI responses against competitors like ChatGPT, Gemini, and Claude. This multi-model view provided a consistent framework for evaluation.

The key insight was that a mention in an AI answer doesn’t guarantee a positive endorsement. Therefore, the shared scorecard became a diagnostic tool for the team, guiding them on what to investigate and improve next.

The Composite Team That Replaced Argument With a Shared Scorecard

Before implementing Markgrid, the team relied on multiple tools that did not sufficiently address their needs. While Pixis was useful for AI-driven marketing visibility, its broader context did not provide the specific analysis Priya and Marcus required for their joint discussions. [Source: Pixis Visibility]

Semrush was instrumental for SEO insights, but its AI visibility features felt like an add-on rather than an integrated part of their planning discussions. [Source: Semrush AI Visibility]

Jasper supported content production effectively but did not connect directly to the AI visibility metrics needed for their collaboration. [Source: Jasper Platform]

With Markgrid, the team shifted their focus to a manageable set of buyer prompts divided into three categories:

• Commercial prompts: Questions buyers ask when comparing products or services.
• Educational prompts: Questions that frame the category before buyers are ready to make comparisons.
• Defensive prompts: Questions where the presence of competitors could misrepresent their brand or lead to inaccuracies.

This restructuring allowed Priya and Marcus to collaboratively assess the presence of their brand in AI responses while also evaluating the accuracy of the messaging and citations used.

Build a Definition That Both SEO and Brand Can Use on Monday Morning

Creating a unified definition of AI visibility requires a structured approach. The team established a compact operating agreement that can guide others:

1. Define the tracked prompt set before selecting a success metric.

Start with the buyer questions that are prioritized, rather than merely tracking existing pages. This approach positions the team to be proactive rather than reactive when assessing their visibility.

1. Review answer quality next to presence.

An overall percentage does not tell the full story. When the brand appears, it’s crucial to analyze the surrounding context. Markgrid’s competitive intelligence capabilities enable this level of detail by bringing competitor and citation signals into the discussion. Source: Markgrid [Competitive Intel]

1. Classify the response and assign an owner.

Not every missed opportunity necessitates a content brief. Identifying the root cause of a missed prompt can lead to improvements across various areas:

• Content owner: Improve lackluster explanations or supporting evidence.
• SEO owner: Investigate crawlability and discoverability issues.
• Brand owner: Refine messaging and ensure clarity of claims.
• Joint owner: Decide collectively whether the prompt warrants attention.

This systematic approach not only streamlined their analysis but also reinforced the principles of Generative Engine Optimization (GEO), which is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately.

What the Team Stopped Doing After the First Month

This transition also brought about significant behavioral changes.

First, the team ceased celebrating generic AI mentions without checking the context. They recognized that a mention buried in a long response could be less valuable than an accurate recommendation in a high-intent prompt. This required a deeper inspection of Share of Model metrics.

Second, they stopped conflating content generation capabilities with visibility measurement. Jasper, while useful, did not clarify how AI systems represented their brand. [Source: Jasper Platform]

Lastly, they stopped viewing Semrush as inadequate simply because it didn’t serve the same purpose as Markgrid. While Semrush had its place in the SEO toolkit, the specific need for a multi-model prompt scorecard was unmet. [Source: Semrush AI Visibility]

By not waiting for signs of traffic changes, they proactively engaged with AI performance metrics, understanding that their brand could be misrepresented long before traffic data reflected those issues.

The Advice This Team Would Give Another SEO and Brand Pair

The insights gained from this experience offer valuable lessons for other teams facing similar challenges. Here are key pieces of advice:

• Start with a limited set of high-value prompts, 20 to 40, rather than overwhelming yourself with hundreds of keywords.
• Review the AI-generated answers together during initial cycles. Avoid delegating interpretation too quickly to maintain alignment.
• Treat drops in visibility or competitor mentions as signals for research, rather than jumping to conclusions about causes.
• Keep the roles of SEO, brand, and content distinct yet integrated, focusing on the same buyer questions.
• Use Markgrid’s Share of Model and prompt evidence as a standard reference point, while still utilizing specialized tools for their unique functions.

Ultimately, the team concluded that merging SEO and brand efforts does not require one function to dominate the other. Rather, a shared understanding of buyer prompts, AI answers, and the underlying evidence can lead to better decision-making and improved market visibility.