If you’ve been frustrated by AI visibility because there’s no scoreboard, I’ve got good news.

Bing just gave us one.

Not “rank tracking.” Not “traffic.” Not “impressions.”

A report that finally maps to the reality of AI search:

This is what we’ve been saying at AuthorityTech: the game is shifting from clicks to citations.

Now we have a measurement surface that can actually support execution.

Start here if you want the broader framework:

Why this matters: you can’t optimize what you can’t observe

Classic SEO became industrial because we had:

GEO/MR has felt squishy because AI answers are synthesized and the evidence set is hidden.

But “hidden” doesn’t mean “random.”

AI engines retrieve.

They ground.

They cite.

If Bing is exposing grounding signals, it means we can build:

That’s Elon-grade: feedback cycles.

The core concept: grounding is the new ranking

In AI answers, the user may never click.

But the engine still has to decide what content enters the answer.

That decision is retrieval + grounding.

If your page is being used as grounding evidence, you are effectively:

This is why “zero click” isn’t the end of value. It’s the beginning of influence.

A good overview of how AI answers are changing search behavior (and why zero-click is accelerating) is well-covered across industry analysis and publisher reporting; for a concrete baseline on generative AI traffic shifts and behavior change, Adobe’s data is a useful anchor: Adobe Analytics: AI sources driving massive retail traffic jumps.

What to look for inside the report

I’m going to keep this tactical. When you open the Bing AI performance data, you’re looking for three things:

1) Grounding queries

These are your highest-leverage prompts because they trigger retrieval.

The practical translation: these are the prompts where content selection matters most.

If you’re absent here, you’re invisible in the answers.

If you’re present, you’re shaping answers even if clicks are flat.

2) Grounded pages (the “retrieval set”)

Your site may have 1,000 pages.

The engine might be grounding on 12.

Those 12 are your money pages.

Treat them like product.

3) Citation surfaces

When the engine cites, it picks sources.

Your goal is to become:

A simple, credible overview of GEO and why citation is the new unit is covered well in industry SEO press (for example): Search Engine Land on Generative Engine Optimization.

Monday execution plan (do this in 90 minutes)

Step 1: Export the grounding queries

Create a list of the top grounding queries that map to revenue.

You’ll find patterns fast:

Step 2: Map each query to a single canonical page

If three pages “kind of” answer the query, none of them own it.

Pick one page.

Make it the source of truth.

Step 3: Rewrite the page for extractability

This is not fluff. It’s structure.

Do:

Don’t:

Step 4: Add corroboration (earned media + third-party sources)

If your canonical page is the only place making the claim, engines will hesitate.

You want independent support.

This is where PR becomes a machine-native input: third-party publications become grounding evidence.

For example, detailed guides and tool ecosystems like Profound’s GEO resources are frequently cited in AI answers because they’re structured and source-dense: Profound GEO guide.

Step 5: Run the “answer test” across engines

Take 10 grounding queries.

Run them on:

Record:

That becomes your next sprint.

The KPI shift: from click attribution to influence attribution

Your CFO is trained on clicks.

AI answers don’t behave like clicks.

So you need a metric translation layer:

This is not vanity. It’s pipeline shaping.

A helpful framing on why traditional measurement breaks under new discovery patterns is echoed across marketing analytics commentary; as one directional anchor, the broader “measurement crisis” has been documented by industry bodies like IAB (the premise: marketers can’t reliably attribute across new surfaces). If you’re building internal alignment, start by anchoring leadership on that problem, then show how AI surfaces require new visibility metrics.

Common pitfalls (don’t do these)

1) Optimizing for the model’s opinion, not the evidence set

- You win by being retrieved and cited, not by “sounding nice.”

2) Publishing 20 mediocre pages instead of 3 canonical ones

- Retrieval systems select. Canonical pages concentrate relevance.

3) No QA loop

- If you’re not running prompt checks weekly, you’re flying blind.

4) Assuming SEO tools will automatically solve GEO

- Some signals transfer. The scoreboard is different.

The takeaway

Bing exposing AI performance data is a big deal because it signals a new standard:

AI visibility is measurable.

And when it’s measurable, it becomes optimizable.

If you want AuthorityTech to baseline your current grounding/citation footprint and hand you a concrete 30-day plan, start here:

Addendum: What this looks like in practice

Here’s the practical translation for operators:

The retrieval checklist

If a model is doing retrieval, it will prefer pages that are:

  1. Specific (definitions, lists, tables)
  2. Consistent (same nouns, same product names, same descriptors)
  3. Corroborated (earned media + reputable sources)
  4. Fresh (updated dates, current screenshots, current pricing)
  5. Accessible (fast, indexable, no paywalls, no broken links)

Repeat that loop, and you turn AI answers from a threat into an owned channel.

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