What’s Included in an AI Visibility Audit & Strategy: A Phase-by Phase-Breakdown
“How do we make our brand visible in the eyes of AI tools?”
That’s the question almost every marketer and brand executive is asking, and the one we’ve been actively working to answer at Masthead.
Over the past year, we’ve been deeply embedded in understanding how large language models take complex human questions and turn them into highly personalized answers. Those answers increasingly shape how people discover brands, evaluate options, and decide what to trust, usually before they ever come close to your website.
Nearly every company wants their products and services to show up in those moments, but many are still operating without a clear view of how AI systems are actually referencing them. They don’t know exactly where they appear, how they’re being discussed, or which signals and sources are truly influencing inclusion in AI-generated answers.
This gap is exactly why we developed our AI Visibility Audit & Strategy. It’s not a repackaged SEO audit or a theoretical take on how AI might work. It’s a focused, repeatable framework built from real-world testing and cross-platform analysis, designed to clarify where brands stand today. Our goal is to help clients understand what would meaningfully improve their visibility going forward.
If you’re considering an AI Visibility Audit & Strategy for your brand, the work breaks down into six phases.
In approximately 6-8 weeks, you’ll get a precise snapshot of how your brand is represented in AI-generated answers today, along with an actionable set of recommendations for improving that visibility.
Phase 1: AI Platform & Query Landscape Definition
Before measuring visibility, we start by grounding the audit in how AI-driven discovery actually works for your category. Different AI platforms behave differently, rely on different sources, and respond to different types of prompts. This first phase ensures we’re evaluating visibility in the places and moments that matter most for your audience.
In this phase, we focus on:
Identifying the AI platforms most relevant to your audience and buying journey
Developing a representative set of queries that reflect how people really ask questions in AI tools today, including branded, unbranded, and high-intent decision queries
Grouping those questions into topic areas tied to education, evaluation, and decision-making moments
By the end of this phase, we’re testing the right questions, in the right environments, with confidence that the analysis reflects real user behavior—not assumptions.
Phase 2: Baseline AI Visibility & Share of Voice Analysis
Once we’ve defined the right platforms and questions, we establish a clear baseline for how your brand actually shows up in AI-generated answers today. This phase is about moving from assumptions to evidence.
Here, we look at how often your brand appears across the query set, the contexts in which it’s mentioned, and how that visibility compares to competitors. Rather than relying on modeled estimates alone, we analyze real AI outputs to understand what users are actually seeing.
In this phase, we focus on:
Measuring brand presence across branded, unbranded, and competitive prompts
Assessing relative share of voice compared to key competitors in your category
Identifying platform-specific visibility patterns, including strengths, gaps, and blind spots
This gives you a defensible snapshot of current AI visibility—one that can serve as a true baseline for future strategy and measurement.
Phase 3: Citation, Source & Influence Mapping
AI-generated answers don’t appear out of nowhere. They’re built on a network of sources that models consistently rely on and trust. Think of those sources as a “panel of experts” assigned to answer user questions. This phase is about understanding what’s actually powering answers in your category—and which sources are on that panel.
We analyze which third-party domains, publishers, and platforms are most frequently cited by AI tools, and how your brand fits into that ecosystem. This work is based on observable AI outputs and recurring patterns across prompts and platforms—not assumptions about proprietary model training data. This often reveals why certain competitors surface more consistently—and where external influence plays a disproportionate role.
In this phase, we focus on:
Identifying the third-party domains and platforms most frequently cited in AI-generated answers
Evaluating which owned pages are referenced, and which are consistently overlooked
Analyzing co-citation patterns to understand how brands are positioned relative to one another
The result is a much clearer picture of where AI systems are sourcing authority—and where influence is being established outside your own site.
(If you’re curious how this plays out for your own brand, our AI Visibility Audit & Strategy is designed to answer exactly these questions. You can also reach out if you’d rather talk it through.)
Phase 4: Narrative Accuracy & Brand Framing Review
Visibility alone isn’t enough. When AI tools mention your brand, they’re also summarizing, paraphrasing, and framing what you stand for—often shaping perception before a user ever clicks through.
In this phase, we examine how AI systems describe your brand across platforms and prompts, and whether that narrative aligns with how you want to be understood.
In this phase, we focus on:
Reviewing AI-generated descriptions of your brand across multiple platforms
Assessing tone, authority, and the role your brand is positioned to play within the category
Identifying narrative drift, oversimplification, or competitive bias in AI summaries
This work clarifies how your brand is actually being represented today—and where narrative correction or reinforcement would have the greatest impact.
Phase 5: Topic Coverage & Content Structure Gaps
When brands fail to surface in AI-generated answers, the issue is often less about missing content and more about how that content is structured, framed, or distributed.
This phase connects visibility gaps back to content realities, helping distinguish between true coverage gaps and content that exists but isn’t retrievable or influential in AI systems.
In this phase, we focus on:
Identifying high-value topic areas where competitors dominate AI-generated answers
Evaluating existing content for depth, clarity, and structural alignment with AI retrieval
Distinguishing between under-structured content and genuine gaps that require new investment
The outcome is sharper clarity around where content effort should be concentrated—and where it’s unlikely to change visibility outcomes.
Phase 6: Strategic Synthesis & Visibility Roadmap
The final phase brings everything together. Rather than delivering a long list of disconnected findings, we synthesize insights across platforms, prompts, sources, and narratives into a focused strategic roadmap.
This phase is about prioritization: deciding where to focus first, what to fix, and what to build next.
In this phase, we focus on:
Prioritizing AI visibility opportunities based on potential impact and urgency
Recommending strategic actions across content creation, restructuring, and external influence
Establishing a foundation for ongoing AI Visibility Strategy support
You’re left with a clear, actionable plan grounded in how AI systems actually behave today—and a shared understanding of what it will take to improve visibility over time.
Because AI systems are probabilistic and continually evolving, no brand can fully control how or when it appears in AI-generated answers. Our work doesn’t attempt to “force” inclusion or reverse-engineer proprietary models. Instead, the audit focuses on identifying the signals, sources, and narratives that consistently influence visibility, so teams can make informed, high-leverage decisions that improve outcomes over time.
AI Visibility Audit & Strategy: Frequently Asked Questions
How long does an AI Visibility Audit & Strategy take?
A full AI Visibility Audit & Strategy typically takes six to eight weeks. That timeline reflects the depth of analysis required to understand AI visibility in a meaningful way.
During that time we’re aligning with key stakeholders, defining the right query and platform landscape, running both automated analysis and hands-on prompt testing, and evaluating patterns across platforms rather than isolated examples. We also spend time converging findings across visibility, sourcing, and brand framing so the final strategy reflects how AI systems actually behave (not a moment-in-time snapshot).
What stakeholders are typically involved in an AI Visibility Audit & Strategy?
The most effective engagements involve the teams that directly shape how a brand shows up in the world: marketing leadership, content or editorial leads, and demand generation or growth teams. SEO or digital experience stakeholders are often involved as well, depending on the organization.
AI visibility sits at the intersection of these functions. Bringing the right people into the conversation early helps ensure the strategy can actually move forward, rather than stalling once recommendations are delivered.
How is an AI Visibility Audit different from a traditional SEO audit?
A traditional SEO audit evaluates how your site performs once someone is already searching for you or your category—rankings, crawlability, backlinks, and traffic performance.
An AI Visibility Audit looks further upstream. It examines whether and how AI systems introduce your brand into the decision-making process in the first place. That includes whether your brand is included in AI-generated answers at all, how it’s framed when it does appear, which third-party sources models rely on when forming responses, and where competitors are shaping understanding before a user ever conducts a traditional search.
In practice, AI visibility often influences the searches that come later—branded queries, comparison searches, and downstream demand that SEO ultimately captures. The audit complements SEO by focusing on the signals that shape perception and preference before a website visit ever occurs.
Do you use automated tools, manual testing, or both?
We use both. Tools help surface patterns and potential opportunity areas at scale, while manual prompt testing is essential for understanding what AI systems are actually showing users today.
Because AI outputs vary by platform, phrasing, and context, this combination allows us to validate real brand presence, assess narrative accuracy, and understand citation behavior in a way automation alone can’t provide.
How often should an AI Visibility Audit or Strategy be updated?
The initial audit establishes a baseline, but AI visibility isn’t something most teams should treat as a one-time exercise. Platforms evolve, competitors adapt, and content ecosystems change quickly.
Many organizations choose to manage AI visibility as an ongoing strategic discipline through monthly strategy hours. This allows teams to refine priorities in near real time, respond to platform changes, and avoid disruptive, large-scale strategy resets.
Does AI Visibility Strategy replace SEO?
No. AI Visibility Strategy doesn’t replace SEO, it expands the role SEO plays in a world where decisions are increasingly shaped before a website visit happens.
SEO remains critical for capture, authority, and conversion once demand exists. AI Visibility Strategy focuses on the signals that influence how that demand is formed in the first place: how brands are introduced, framed, and reinforced in AI-generated answers.
The strongest outcomes come when AI visibility and SEO are treated as part of the same ecosystem, with each supporting a different stage of discovery and decision-making.
What industries or business models benefit most from an AI Visibility Audit & Strategy?
This work is especially valuable in high-consideration categories, where buyers seek education, comparisons, and reassurance before engaging directly with a brand. Financial services, healthcare and wellness, B2B technology, education, and other complex decision-making categories often see the most impact.
In these environments, AI systems increasingly shape understanding early in the journey, making accurate visibility and framing particularly important.
How much does an AI Visibility Audit & Strategy cost?
A comprehensive AI Visibility Audit & Strategy typically starts at $25,000 for a deep, multi-platform analysis and strategic roadmap.
For teams managing AI visibility on an ongoing basis or with more targeted needs, we may also support hourly or retainer-based strategy engagements, depending on scope and goals.
If you’re starting to think more seriously about how AI systems represent your brand—and want a clearer view of where you stand today—let’s talk. You can set up time directly with us here and we’ll get into more depth about AI Visibility Strategy and your brand’s specific goals.
