Anastasia Braitsik stands as a towering figure in the rapidly evolving landscape of digital marketing analytics, particularly as traditional search gives way to the intricate mechanics of AI retrieval. With a seasoned background in content strategy and technical SEO, she has spent years deciphering how algorithms perceive, categorize, and prioritize brand information. Her recent focus on the intersection of user intent and machine learning has made her an essential voice for organizations trying to navigate the transition toward generative search experiences. In this conversation, she breaks down the revolutionary shifts occurring within tools like Microsoft Clarity, specifically focusing on how marketers can finally distinguish between being sought out by name and being discovered through category expertise. This distinction is no longer just a technical nuance; it is the cornerstone of how modern leadership will justify content investments in an AI-first world.
The discussion centers on the August 3 update to the AI Citations dashboard, which introduces granular filters for branded and non-branded queries. We explore the tactical significance of grounding queries, the specific metrics behind the “Share of Authority” reporting, and the divergence between citations and actual referral traffic. Throughout the interview, the focus remains on how these data points should influence a company’s documentation, category content, and overall digital PR strategy.
Branded grounding queries often lead AI to product specs or pricing pages, whereas non-branded queries highlight something entirely different; how should marketers interpret this distinction to improve their strategy?
When an AI system triggers a branded grounding query, it acts like a meticulous researcher looking specifically for your credentials, seeking out your pricing pages, product specifications, or support articles to validate a response. This is a moment of deep brand retrieval where the system is confirming your specific identity, often looking at your company policies or comparisons to provide a factual answer. Conversely, non-branded queries represent a much broader, exploratory stage where the AI is investigating category-level topics, such as “enterprise analytics platforms” or “methods for understanding website behavior.” For a marketer, this distinction is vital because it reveals whether you are simply a known entity or if you are being treated as a topical authority that the AI trusts to explain a wider problem. Analyzing these two different types of visibility allows you to see exactly where your brand enters the research process and where you might be losing ground to third-party publishers who are capturing that broader category research.
The concept of Share of Authority is central to this update, but how does it differ from traditional market share, and why does that nuance matter for a Chief Marketing Officer?
It is important to clarify that Share of Authority in Microsoft Clarity is not a percentage of the entire AI search market, but rather a calculation of how much visibility you earned within the specific queries where your domain appeared. Microsoft calculates this daily, looking at “query-days” when your domain received a citation and comparing it against all other domains cited in those same queries. For a CMO, this metric serves as a high-fidelity competitive signal because it shows your relative strength against rivals in the exact conversations where you are already a participant. It moves the needle from a vague “we are being seen” to a specific “we own 40% of the citations in this specific category search.” This allows for a much more tactical allocation of resources, as you can see precisely which topics are driving your authority and where a competitor might be capturing the AI’s attention more effectively.
There is a fascinating gap between what a user types and what an AI actually searches for; why is it vital for teams to look at grounding queries rather than just the initial prompt?
The user’s prompt is just the tip of the iceberg, whereas the grounding query is what happens beneath the surface when the AI system searches for web content to generate an answer. For instance, a user might ask a generic question like, “Which tools can help me understand how visitors use my website?” and the AI might then perform a grounding query for several specific product names behind the scenes. Clarity classifies these as branded queries even though the user never mentioned a brand, which tells us more about the AI’s internal research behavior than the user’s initial intent. Relying solely on prompts can be misleading because they don’t reveal the AI’s preference for specific sources or how it bridges the gap between a problem and a solution. By focusing on grounding queries, marketers can understand how the AI’s retrieval infrastructure perceives their brand’s relevance to a topic, which is a separate layer of performance from customer awareness.
We often see high citation counts that do not translate into traffic; how can a marketing team bridge the gap between AI visibility and actual business results like sales or leads?
It is a common pitfall to assume that a high citation count automatically translates into a flood of new customers, but the reality is much more nuanced. You must track performance across three distinct stages: citation visibility, AI referral traffic, and final business results like leads or sales. A citation simply means an AI referenced your page, but if the AI’s answer is comprehensive enough or the source link is buried, the user might never feel the need to click through, resulting in a session with no referral. This is why we must use our own analytics and CRM platforms to connect the sessions that actually arrive from AI assistants back to the revenue pipeline. By keeping these metrics separate, you avoid the dangerous mistake of presenting citation growth as a proxy for proven business growth, ensuring your team stays focused on the bottom line.
If a brand notices a sudden dip in their non-branded Share of Authority, what specific tactical moves should they prioritize to regain that expertise-driven ground?
A decline in non-branded Share of Authority is a clear signal that your content is no longer the “go-to” source for broader category research, which usually requires a shift in your content distribution or depth. You should immediately look at which competitors or third-party sources are earning those citations and determine if your content lacks the original research or categorical expertise they are providing. This might mean investing in more robust white papers, industry reports, or “how-to” guides that address the problem-solving stage of the buyer’s journey rather than just the product features. Furthermore, you should verify your domain through Google Search Console or Bing Webmaster Tools to ensure Clarity is capturing your data correctly, then focus on creating high-quality, non-branded content that addresses the specific use cases the AI is currently prioritizing.
What is your forecast for how AI retrieval behavior will change the way we approach technical SEO and content depth over the next year?
I believe we are moving toward a period where “retrieval-readiness” will become as important as traditional keyword ranking, forcing us to prioritize the structured clarity of our documentation above all else. As AI systems become more sophisticated in their grounding processes, brands that provide the most accessible, fact-dense pricing, specifications, and policy pages will dominate the branded citation space. We will see a shift where technical SEO isn’t just about site speed and crawlability, but about how easily an LLM can parse and cite your data to ground its answers in truth. Ultimately, the winners will be the marketers who stop treating AI visibility as a single metric and instead start managing it as a multi-layered ecosystem of citations, referrals, and conversions.
