TL;DR
Bing has launched the AI performance metric in Bing's Webmaster (similar to Google's Search Console). Bing appears to be the first major search platform to provide citation-level AI visibility data similar to Search Console metrics. This is a cutting-edge feature that provides insight into how your website's content is utilized for AI search.
This article explored its:
- Key Features
- Comparison between Search Performance and AI Performance
- Actionable strategies to optimize the content
Quick Overview: What is AI Performance in Bing Webmaster Tools?
AI Performance in Bing Webmaster Tools shows how your website content is utilized in AI-generated answers across Microsoft Co-Pilot and partner experiences. The partner AI experience includes the following:
- Microsoft Co-pilot
- AI-generate summaries in Bing
- Select Partner AI integrations
It provides data on citations, avg. cited pages, clicks, and others with AI- driven search. This feature is designed to help marketers, developers, and content creators optimize their content for AI-driven search experiences. To see metrics for your website, log in to the Bing Webmaster tool and navigate to the AI performance section. If it will be your first time, then you will need to set up webiste through verification.
Key Features of AI Performance in Bing Webmaster Tools
Previously, we had to use prompts to understand what generative engines think about a brand. It was more like sentiment analysis. There were no tools to measure how the content is being used to cite. It was not possible to track which pages are being cited, what are the queries are being used to cite, and what data to track. We can only track if any user comes to our page through a click from an AI search result. AI Performance in Bing has solved these problems with its tracking metrics. Although it was launched on 10th February 2026, you can track the data from 1st of November 2025.
Let's look into what metrics are being tracked.
Pages cited in AI Answer
It tracks how often a specific page is being cited in an Answer Engine. This can help us to understand the performance of each article. It will help us to evaluate the individual performance of an article. By analyzing this data, we can:
- Identify high-performing content
- Topics preferred by AI answer systems
- Discover patterns in structure, formatting, or content depth
- Reverse-engineer successful articles to improve future articles and content
Average Cited Pages
It is the average number of unique pages from your site that were cited per day in AI-generated answers. This will give us insight into how good AI trusts our content. It will also help us to understand how frequently AI systems reference our content. For example:
- If your website has 50 total pages
- And on average, you are getting 5 unique page citations
That means 10% of your content is being referenced daily. A higher percentage suggests that:
- More of your content is being used by AI to answer systems
- Your site is considered relevant and useful for answering queries.
Grounding Queries
Grounding queries are the key phrases used by the Answer Engine to retrieve the content from your site to cite. It is not the user queries that are being used, but how AI has parsed the user queries. This will be helpful to identify the user intent over the keywords. It can be based on:
- Intent (informational, transactional, comparison, etc)
- Context
- Sentiment
- Semantic meaning
Analyzing grounding queries will help in:
- Discovering semantic variation that triggers citation
- Optimizing content for meaning, not just for keywords
- Understanding how AI interprets user intent
Bing Docs focuses more on the Grounding queries. It may eventually replace traditional keyword metrics in the GEO era.
Additional metrics and features include:
- Total Citations: You can get the number for Total citaitons and Average cited pages over a selected period of time
- Visibility trends over time: You can track the citations and cited pages metrics using a graph over a selected period of time.
- Downloading Data: You can download data for citations, grounding queries, and per page citation in CSV format for further analysis.
Comparing Search Performance vs AI Performance
Since Bing now provides both Search Performance and AI Performance data, we can compare them over the same time period. This allows us to clearly understand the differences between traditional search results and AI-driven visibility. This comparison is for my technical blogging website, surajon.dev. This comparison is for the last 3 months.
Dataset Overview Comparison
| Metric | AI Performance (Answer Engines) | Search Performance (SEO) |
|---|---|---|
| Total Queries / Keywords | 31 | 1,598 |
| Average Visibility Metric | 20 citations per query | 5.5 impressions per keyword |
| Median | 10 citations | 1 impression |
| Maximum Outlier(Maximum number per day) | 128 citations | 1,089 impressions |
| Distribution Type | Concentrated | Highly fragmented |
| Traffic Pattern | Few queries dominate | Long-tail spread |
More keywords rank for Search performance is due to the fact that even if your page rank is 60th, it will be given an impression. But with AI performance, it will only link to very few pages that is of high quality.
Distribution Behaviour
| Factor | AI Search (Answer Engines) | Traditional SEO (Search Engines) |
|---|---|---|
| How many queries drive results? | A small number of queries drive most citations. | A very large number of keywords drive small amounts of impressions each. |
| Traffic concentration | Traffic is concentrated on a few strong topics. | Traffic is spread across hundreds or thousands of small keywords. |
| Performance pattern | Some queries get cited many times repeatedly. | Most keywords get 1–2 impressions and very few clicks. |
| Impact of top performers | Top-performing queries have a very big impact on total AI visibility. | Even top keywords usually don’t dominate overall traffic. |
| Long-tail effect | Weak long-tail presence. AI focuses more on main intent clusters. | Very strong long-tail presence. Many small variations bring small traffic. |
| Growth behavior | Growth happens by strengthening topic authority. | Growth happens by ranking for more keyword variations. |
This clearly shows how AI search is different from traditional search. Based on the above comparisons of the data, we can clearly state the following:
- AI is selective: It will not cite all the phrases/keywords, but only certain topics will be cited repeatedly.
- SEO is fragmented: It ranks for multiple keywords but has low individual impact.
- Citation concentration exists: AI visibility is not evenly distributed across all your content. Instead, a small number of queries might generate most of your AI citations. It is also due to the fact that I never optimized my content for Generative Engines.
- Authority Clustering: Certain topic clusters will likely be more dominant than others.
- AI ≠ SEO performance: From the data, we can surely say that high impression does not guarantee AI citations.
Using AI Performance to Improve Your Visibility
Based on our analysis and the docs provided by Bing, we can pinpoint methods that can drive AI citation. This will include how you can use Bing's AI performance to increase citations. Here are the best practices:
- Align content with user intent: By reviewing ground queries, you can understand what phrases are driving the citation. This helps us to align our content to the user intent and what kind of content/information supports AI answers.
- Strengthen depth and expertise: Content that shows depth and expertise will be cited more. You should cover all the major subtopics that belong to a topic.
- Clarity and Structure: Your content should have clarity and proper structure for the heading. Including lists, tables, and FAQ-style content will make the content easier to understand and refreshed by AI.
- Links: Whenever you claim something in the content, you should attach a link to that site. This will improve clarity and trust when content is reused in AI-generated answers
- Freshness and Accuracy: Content that is fresh, i.e., up-to-date with present data, will be cited more. Accuracy matters as AI will try to minimize the errors and will only pick accurate content.
- Content Density over Length: Content that is information dense will be prioritized over that that just use repetative information that is longer.
Frequently Asked Questions
What is AI Performance in Bing Webmaster Tools?
Bing has launched AI performance to track citation, average citaion and grounding queries across Microsoft's Copilot and partner experience.
How does AI Performance help with content optimization?
Tracking AI performance, such as citation, average citaion and grounding query, helps us in identifying patterns and which type of content is cited more often.
Can small websites benefit from AI Performance?
Small websites can take early advantage by utilizing AI performance for tracking citation, average citaion and grounding query. You can target a topic to match the topical authority.
Is AI Performance a replacement for traditional SEO tools?
No, it is another layer of the search. AI Performance focuses on citation, while SEO tools focus on traditional search ranking.
What metrics does AI Performance track?
The following metrics are tracked by the AI performance:
- Pages cited in AI Answer
- Average Cited Pages
- Grounding Queries
- Total Citations
Conclusion
Bing's AI performance features mark a significant shift in how we measure visibility in the generative search era. This is the first time that website owners can see how their content is being used to cite in the Answer engine. Understanding the difference between the traditional SEO and AI-driven GEO will allow marketers, developer and content creators to move beyond rankings.
Looking ahead, this is likely just the beginning. As AI-driven search continues to grow, other platforms such as Google, OpenAI, and additional AI-integrated ecosystems may introduce similar citation-level reporting like Bing's AI Performance in Webmaster tools. Websites that adapt early by strengthening topical authority, improving structure, and optimizing for grounding queries will have a strong advantage.
Thanks for reading the article.