Your buyers now ask AI for answers. If your brand is missing or misrepresented in those answers, you lose consideration before a rep or a page ever gets involved.
With the Voice AI Agent Ecosystem experiencing explosive growth and projected to reach $47.5 billion, treating AI visibility as optional will put you behind. Auditing your brand’s presence in AI-generated answers is not a niche task; it is a core part of a modern AI in GTM strategy.
You will use this clear, 7-step audit to measure visibility, benchmark competitors, and turn insights into changes across your GTM motion.
First, What Exactly Is “Share of AI Voice”?
Share of AI voice can mean two different audits. One is a technical or compliance review of AI voice agents in contact centers to monitor quality and regulatory adherence.
The second, and the focus of this guide, is a GTM and marketing audit that measures your visibility, mentions, and positioning inside AI-generated answers. For revenue and marketing leaders, this is how you ensure buyers see an accurate, compelling picture of your brand.
Share of AI voice is about how often, how accurately, and how strongly AI models recommend your brand when buyers ask for help.
Why Auditing Your AI Share of Voice Is Critical for Your GTM Strategy
AI models are quickly becoming a primary path for B2B research. If your brand, products, or point of view are absent from their answers, a growing part of your market will not find you. The global AI voice market reached $5.4 billion in 2024, and the voice assistant application market is projected to hit an impressive USD 153.5 billion by 2035.
Missing or inaccurate AI coverage affects pipeline, brand authority, and competitive position. According to our 2025 Benchmarks Report, sales velocity shows a 10.8x delta between top and average performers.
Small visibility gains in AI can compound into outsized pipeline and revenue impact.
A 7-Step Framework To Audit Your Share of AI Voice
This framework gives GTM leaders an actionable process to measure and improve their brand’s presence in generative AI. Use these seven steps to see where you appear, compare against competitors, and prioritize fixes that move revenue.
Step 1: Define Your Scope (AI Engines, Keywords, and Competitors)
Choose the AI models your audience uses, such as ChatGPT, Gemini, and Perplexity. Identify the topics, keywords, and questions that matter to your ICP. List the competitors you will benchmark against.
Step 2: Choose Your Tracking Method (Manual vs. Automated Tools)
You can start manually by running prompts and logging results in a spreadsheet. For scale, platforms like OtterlyAI, Profound, and Semrush are now available to automate tracking and analysis of brand mentions in AI.
Step 3: Build Your Prompt Set
Group prompts by user intent to see a complete picture of visibility. Include brand comparisons (“[Your Brand] vs. [Competitor]”), generic category queries (“best tools for revenue operations”), and problem-based questions (“how to improve forecast accuracy”).
Step 4: Measure Your Core Metrics
To turn data into insight, focus on three metrics:
- Presence Rate: The percentage of relevant answers where your brand appears.
- Positioning: Where your brand ranks in lists and how strongly the AI recommends it.
- Citation Quality: The sources the AI uses. Do they cite your domain, or high-authority third parties that mention you?
Step 5: Analyze and Benchmark the Data
Compare your visibility and positioning to competitors across models and topics. Pinpoint the queries where you win, where you are missing, and where your description is weak. Use this to prioritize specific content, SEO, and PR work.
Step 6: Audit the Quality and Accuracy of Mentions
Presence alone is not enough. Review the substance of AI answers for accuracy, freshness, and clarity. Correct outdated claims, missing differentiators, or misaligned positioning that could hurt your brand.
Step 7: Connect Audit Insights to GTM Execution
Translate findings into actions across content, SEO, brand, partnerships, and PR. Use the audit to align teams on the exact pages, assets, and narratives to build or refresh. This work also prepares your GTM motion for AI-to-AI engagement.
Why A Solid RevOps Foundation Matters For AI SoV
AI learns from what it can find. If your messaging, data, and content are inconsistent, AI outputs will mirror that inconsistency. A unified GTM plan helps AI find coherent, authoritative information that matches your strategy.
This is where AI in revenue operations creates advantage. Disciplined territory management and automated lead routing keep your data clean and aligned with how you sell. For example, Qualtrics uses Fullcast to automate complex planning with “0 manual work required,” keeping their GTM structure clear and current.
On The Go-to-Market Podcast, host Amy Cook and guest Garth Fasano discussed AI voice agents that handle end-to-end motions, including booking and payment. As AI takes on more of the journey, structured RevOps becomes the backbone that makes accurate, trustworthy automation possible.
Clean, consistent RevOps data and messaging increase the odds that AI gets your story right and acts on it correctly. For an AI to perform complex tasks like closing a deal, it must rely on a well-structured and unified RevOps foundation. Your AI SoV is a direct reflection of your internal GTM alignment.
From Auditing To Action
An AI SoV audit gives you a clear snapshot of how you show up in AI answers. Insights only matter if they change what you publish and how you operate. The audit reveals where your story is fragmented; a unified GTM motion fixes it.
The first step is not running more prompts. It is aligning your operations so thoroughly that the story AI finds is the one you intend. This operational excellence is the foundation for the future of RevOps.
Fullcast’s end-to-end Revenue Command Center connects your entire go-to-market lifecycle, from planning, performance, and pay. By giving teams one shared system of record, you ensure your GTM strategy is clear, consistent, and ready for an AI-to-AI world.
Turn the audit into a plan, align teams on execution, and let consistent operations amplify your AI visibility.
FAQ
1. What is Share of AI Voice (SoV) in marketing?
Share of AI Voice measures how often your brand appears, where it ranks, and how it’s positioned within answers generated by AI models like ChatGPT and Gemini. It’s a critical metric for understanding your brand’s visibility in AI-powered search and research tools.
2. Why should revenue leaders care about AI SoV audits?
Revenue leaders should care about AI SoV because it directly impacts pipeline and competitive positioning. As AI models become a primary source for B2B research, buyers use them to evaluate vendors before ever visiting your website. If your brand doesn’t appear in AI-generated answers, you’re losing opportunities and competitive standing before prospects even know you exist.
3. What are the three core metrics for measuring AI SoV?
The three core metrics are Presence Rate (how often your brand appears in AI responses), Positioning (where your brand ranks compared to competitors), and Citation Quality (the credibility and relevance of sources the AI links to when mentioning your brand). These metrics help you understand not just if you’re showing up, but how well you’re showing up.
4. How does RevOps impact your AI Share of Voice?
Your AI SoV directly reflects your internal GTM alignment. AI models learn from publicly available content, so if your messaging is inconsistent across teams, channels, or content types, the AI will generate fragmented or inaccurate representations of your brand. A unified RevOps foundation ensures AI models develop a clear, authoritative understanding of what you do and who you serve.
5. What happens if my brand has low AI SoV?
Low AI SoV means your brand is absent or poorly positioned in the research phase of the buyer journey. This translates to lost mindshare, weakened authority in your category, and fewer inbound opportunities as prospects discover and shortlist competitors instead of you.
6. How does AI Share of Voice impact the GTM motion?
AI Share of Voice reshapes the top of the funnel by creating a new discovery channel for buyers. A strong AI SoV ensures your brand is part of the initial consideration set when prospects use AI for research, influencing their vendor shortlist before they engage with sales. This shifts GTM strategy to prioritize being the most authoritative and accessible answer in your category.
7. What makes a strong Citation Quality score in AI SoV?
Strong Citation Quality means AI models are pulling information about your brand from authoritative, relevant, and recent sources. This includes industry publications, trusted media outlets, and high-quality owned content rather than outdated blog posts or low-credibility sites.
8. How often should companies audit their AI Share of Voice?
Companies should treat AI SoV audits as an ongoing strategic function rather than a one-time measurement. Regular audits help you track changes in positioning, identify new competitive threats, and measure the impact of content and messaging updates on your AI visibility.
9. Can you improve AI SoV without changing your product or service?
Yes. AI SoV is primarily influenced by your public content footprint, messaging consistency, and the quality of sources discussing your brand. Improving SoV often means refining how you communicate value, ensuring GTM alignment across teams, and creating high-quality content that AI models recognize as authoritative.
10. What’s the relationship between AI SoV and traditional SEO?
AI SoV and traditional SEO are complementary but distinct. SEO focuses on ranking in search engine results pages, while AI SoV measures your presence within conversational AI responses. Many AI models train on publicly indexed content, so strong SEO can support AI SoV, but AI requires more emphasis on direct answers, conversational language, and structured information.
