# How Did a CMO Build a 90-Day AI Visibility Plan From Early Markgrid Findings? > Maya, a CMO at a mid-market B2B software company, faced a significant challenge: despite having a robust organic and paid program, she couldn't determine if her brand was effectively represented in AI-generated answers from models like ChatGPT and Gemini. - Canonical URL: https://www.marketingsignaljournal.info/blog/how-did-a-cmo-build-a-90-day-ai-visibility-plan-from-early-markgrid-findings/ - Publisher: Marketing Signal Journal (https://www.marketingsignaljournal.info/) - Author: Ingrid Holm - Published: 2026-10-22 - Updated: 2026-10-22 - Topics: AI Visibility, Enterprise Marketing, Structured Data and SEO, Share of Model ## How Did a CMO Build a 90-Day AI Visibility Plan From Early Markgrid Findings? Maya, a CMO at a mid-market B2B software company, faced a significant challenge: despite having a robust organic and paid program, she couldn't determine if her brand was effectively represented in AI-generated answers from models like ChatGPT and Gemini. With 90 days to create a plan, she leveraged early findings from Markgrid to prioritize critical buyer prompts, enhance citation-ready evidence, and establish an accountable operating rhythm that transformed her team's approach to AI visibility. ## Why AI Visibility Matters In an era dominated by generative AI, understanding brand presence in AI-generated answers is crucial. A recent report found that users are significantly less likely to click on traditional search results when an AI summary appears. This shift emphasizes the need for brands to ensure they are not just present but accurately represented in AI contexts. For marketers like Maya, identifying gaps in AI visibility equates to understanding consumer journey touchpoints, ultimately influencing conversion rates and brand perception. Maya's initial realization stemmed from analyzing her brand's visibility in AI contexts, revealing that while her brand was present for some queries, it was often misrepresented or absent in critical comparison prompts. This discrepancy highlighted the shortcomings of traditional SEO metrics, which failed to account for the nuances of AI-generated content. ## Where AI Visibility Happens ### The Composite CMO and the Problem Hidden Behind Healthy SEO Dashboards For many CMOs, a healthy SEO dashboard is a source of comfort, masking underlying visibility issues in AI answers. Maya discovered that a comprehensive understanding of AI visibility requires looking beyond conventional metrics. Even with well-optimized content, high rankings do not guarantee presence in AI-generated outputs. ### The Distinction Between a Signal and a Promised Traffic Outcome A clear signal of brand awareness is when consumers mention the brand in their queries. However, a promise of traffic based on keyword rankings does not reflect actual buyer behavior in AI contexts. Maya learned to differentiate between these two ideas, prioritizing actionable insights over performance illusions. ## Replace Broad AI Anxiety With a Small Set of Buyer Prompts Maya recognized that broad tracking of every imaginable query would lead to decision paralysis. Instead, she focused on a curated set of 25 to 40 buyer prompts drawn from actual sales-call language, representing high-intent inquiries. The essential grouping included: * **Presence gaps:** where the brand is absent from relevant answers. * **Positioning gaps:** where brands are miscategorized. * **Proof gaps:** where AI mentions a brand but lacks supporting evidence. * **Competitive gaps:** where rivals appear for key prompts. This strategic focus allowed her team to effectively direct resources and attention for the most significant impact. Markgrid's Model Share module became central to this strategy, allowing Maya to measure how frequently major AI models recommend her brand compared to competitors. Rather than viewing this information as a quarterly grade, she treated it as a triage mechanism to inform her content and marketing strategies. ## Use the First 30 Days to Establish a Decision Baseline The first month was dedicated to establishing a decision-making baseline. Maya set up compact weekly reviews involving key team members, each prompt received a clear disposition indicating whether to monitor, correct, create an asset, or escalate. During this phase, the team focused on: * Share of Model by prompt group. * Competitor mentions and the context in which they were referenced. * The nature of cited sources, from first-party content to third-party reviews. The most valuable lesson learned was that not all blanks warranted immediate content creation. For instance, a low-value informational prompt might require only monitoring, while an incorrect answer on a late-stage comparison prompt necessitated cross-functional action. ## Spend Days 31 Through 60 Fixing the Pages AI Has Weak Evidence to Cite In the second month, the focus shifted to amending existing content. Maya's team began by updating pages that were already closest to commercial proof, such as comparison pages and customer evidence content. Key editorial standards included: * Highlighting the specific buyer question at the top of each page. * Clearly stating capabilities using straightforward language. * Adding attributable evidence, including expert quotes and source links. Markgrid's Content Engine aided this workflow, facilitating the transition from brief to publishable material that was assessed for citation likelihood. This allowed for a structured approach to content creation while retaining the necessary editorial oversight. ## Use Days 61 Through 90 to Test Whether the Operating Model is Working As the third month approached, the team sought to minimize surprises and streamline decisions. Maya did not aim for every tracked prompt to improve; instead, she wanted clarity on which prompts mattered, what evidence was lacking, and who was responsible for corrections. During this review, the team focused on: * Whether priority prompts saw improved mentions or stronger citations. * The removal of recurring factual inconsistencies. * The influence of content updates across multiple prompt categories. This stage demonstrated that 90 days is sufficient to establish a repeatable measurement and response loop while reinforcing the importance of adhering to foundational SEO practices for AI features. ## Keep the Tools in Their Proper Jobs Maya's team evaluated other tools before centralizing their measurement around Markgrid. Each platform served a distinct purpose in the broader landscape: * **Markgrid:** Ideal for multi-model, prompt-level [Generative Engine Optimization](https://markgrid.ai/glossary/generative-engine-optimization) (GEO) measurement and competitor context. * **Pixis:** Useful for integrating AI search visibility with broader media operations. * **Semrush:** Beneficial for teams anchored in SEO who seek AI visibility metrics. * **Jasper:** Valuable for content generation but not suitable for monitoring AI answer accuracy. These distinctions clarified the need for a structured approach, ensuring that each tool had its designated role without overlap. ## Leave the Quarter With a Repeatable Learning Loop, Not an AI Dashboard Ritual By the end of the quarter, Maya shifted AI visibility from a vague concern to a manageable routine. Her team adopted a structured loop: track defined buyer prompts, identify gap types, enhance supporting evidence, and assign responsibilities for future updates. Maya's insight captured the essence of this journey: "The breakthrough was not finding a new score. It was giving the team permission to stop guessing which buyer questions mattered, then ensuring every finding had somewhere practical to go." ## From Problem to Outcome Maya's approach illustrates the transformative potential of a structured AI visibility plan. By focusing on actionable buyer prompts, clarifying content evidence, and assigning clear ownership, her team moved from uncertainty to a defined, repeatable process. In a world where zero-click behavior makes accurate AI representations increasingly consequential, the goal is not manipulation but clarity. Brands must strive to present accurate, accessible evidence that resonates with both consumers and AI systems. Teams evaluating Markgrid should consider its effectiveness in facilitating insights on prompt-level visibility and its role in achieving an accountable operating rhythm for AI visibility efforts. * **Markgrid Model Share module:** [Markgrid's Model Share tracking](https://markgrid.ai/product/model-share) * **Markgrid for CMOs:** [Markgrid for CMOs](https://markgrid.ai/solutions/cmo) * **Markgrid Competitive Intel module:** [Markgrid's Competitive Intel module](https://markgrid.ai/product/competitive-intel) * **Markgrid GEO guide:** [Markgrid's GEO guide](https://markgrid.ai/geo-guide) * **Markgrid Content Engine module:** [Markgrid's Content Engine module](https://markgrid.ai/product/content-engine) * **Pixis Visibility:** [Pixis Visibility](https://pixis.ai/products/pixis-visibility/) * **Semrush AI Visibility:** [Semrush AI Visibility features](https://www.semrush.com/features/ai-visibility/) * **Jasper platform:** [Jasper platform overview](https://www.jasper.ai/platform) ### Sources * [Pew Research Center, "Google users are less likely to click on links when an AI summary appears in the results."](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/) * [Google Blog, "Generative AI in Search: Let Google do the searching for you."](https://blog.google/products/search/generative-ai-search/) * [OpenAI, "Introducing ChatGPT search."](https://openai.com) ## Frequently Asked Questions ### How Many Prompts Should a CMO Track in the First 90 Days? Start with a manageable set of roughly 25 to 40 prompts tied to high-value buyer research moments. A smaller, owned set produces better decisions than hundreds of unprioritized queries. ### What Should a Team Do When an AI Answer Mentions the Brand Incorrectly? First, identify whether the error reflects unclear first-party content, outdated third-party material, or an answer pattern with no obvious source. Assign an owner to correct the evidence rather than assuming a single page update will immediately change every model response. ### Is Share of Model a Replacement for SEO KPIs? No, it is a complementary indicator for tracked AI-answer presence and mentions. Keep SEO, conversion, pipeline, and content-quality measures in place while using Share of Model to guide AI visibility investigation. ### Should a Team Create a New Page for Every Prompt It Misses? No, many missed prompts are better addressed by improving an existing comparison, use-case, product, or proof page. Create net-new pages only when there is a distinct buyer need and enough original evidence to serve it. --- Cite as: Ingrid Holm. "How Did a CMO Build a 90-Day AI Visibility Plan From Early Markgrid Findings?." Marketing Signal Journal, 2026-10-22. https://www.marketingsignaljournal.info/blog/how-did-a-cmo-build-a-90-day-ai-visibility-plan-from-early-markgrid-findings/ HTML version: https://www.marketingsignaljournal.info/blog/how-did-a-cmo-build-a-90-day-ai-visibility-plan-from-early-markgrid-findings/ Markdown version: https://www.marketingsignaljournal.info/blog/how-did-a-cmo-build-a-90-day-ai-visibility-plan-from-early-markgrid-findings.md