The case for adding Bing Webmaster Tools to an SEO report is unglamorous and probably correct: a Google-only view can miss search activity happening somewhere else. A stronger claim sometimes comes next: Bing performance therefore reveals AI visibility. That is where the evidence runs out.

In one discussion, a commenter argues that Bing matters partly because ChatGPT may draw from it. A reply pushes back on the useful point: the exact Bing-versus-Google source mix behind ChatGPT or Gemini is not established in the material. The exchange does not settle how either system works. It marks the line between a number a marketer can observe and an inference the report cannot defend.

Bing performance is Bing performance. That is already useful without turning it into proof of something else.

Use Bing to widen the search view

One commenter argues that SEO reporting should include Bing alongside Google because a single search engine can leave part of the discovery picture out. That is a defensible reason to compare the tools. It does not require a new all-in-one score or a claim about how an AI assistant chooses its sources.

The reporting decision is straightforward. Show what changed in Google. Show what changed in Bing. Compare the queries and pages where the difference matters. If the two engines tell different stories, investigate the difference instead of averaging it away.

This keeps the conclusion inside the data available. A gain in Bing can inform search work. It cannot, by itself, verify that an AI system has discovered, cited, or recommended the brand.

Clarity answers the next question

Two commenters recommend Microsoft Clarity alongside Bing Webmaster Tools. Their value is in the separation. Webmaster data describes search performance; behavioral analysis helps explain what visitors do after they arrive.

Those views belong in the same reporting conversation, but they do not mean the same thing. Search data can show where discovery occurred. Behavioral data can show where visitors hesitated, continued, or left. Keeping the jobs distinct makes it easier to identify which part of the journey actually changed.

An LLM summary is still an interpretation

One commenter describes exporting webmaster data to a general-purpose LLM and warns that the resulting advice can be poor. That contradiction is more useful than another automated dashboard feature.

An LLM can organize observations and propose questions. It cannot decide whether a Bing gain offsets a Google decline, whether an on-page behavior matters commercially, or whether Bing performance proves AI visibility. Those conclusions require evidence and judgment outside the generated paragraph.

The useful boundary is simple: report the number as a number, label the interpretation as an interpretation, and do not let a polished summary convert one into the other.