Last updated September 2026
Most AI visibility programs produce two kinds of reports. The first is a raw export from a monitoring tool: a wall of prompt-level rows that means nothing to anyone outside the team that built it. The second is nothing at all, because the analyst never turned the data into something a CMO would open.
There is no standard format for this report yet. AI visibility is a new discipline, and most teams are improvising a deck the night before a leadership review. This guide gives you the standard: five metrics, one page, one slide, and a caveat line that keeps the numbers honest.
Why most AI visibility reports lose the room
A CMO does not want forty prompt results. They want four answers: are we visible, are we cited, how are we described, and is any of this showing up in traffic.
Two mistakes sink most reports before either of those answers lands.
The first is a wall of raw data with no benchmark. A number without something to compare it against, “34% share of voice,” tells an executive nothing about whether that is good.
The second is a single blended score across all engines. Brand mentions do not agree across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Microsoft Copilot. A blended average hides exactly the gap a CMO needs to see: the one engine where your brand is invisible.
A report that leads with a benchmark instead of a bare number earns more trust in the room. Show a 34% share of voice next to a rival’s 41%, and the number becomes a gap to close. Show it with nothing to compare it against, and the first question back is always the same: is that good?
The five metrics your report needs
Build the report around these five rows, and nothing else. Each one answers a specific executive question.
| Metric | Executive question it answers | What it measures |
|---|---|---|
| Share of voice | Are we visible relative to competitors? | % of relevant prompts where your brand is mentioned, versus your named competitive set |
| Citation share | Are we a source, not just a mention? | % of AI answers that link to or reference a page on your domain |
| Sentiment split | How are we described when we do show up? | The three-way split of mentions: positive, neutral, negative |
| Engine coverage | Where are we winning, and where are we absent? | Which of the five major engines mention your brand this period |
| AI referral traffic | Is any of this reaching the website? | Sessions from AI platforms as a % of total site traffic |
Everything else, prompt-level detail, individual response transcripts, competitor deep dives, belongs in an appendix. Keep it out of the page the CMO actually reads.
How to build the report in six steps
Step 1: Pull share of voice and citation share for the month
Start with the two numbers that answer “are we visible” and “are we a source.” Pull both from your monitoring platform’s share-of-voice or citation report, filtered to the same prompt set you tracked last month.
Otterly.AI and Temso both keep a running share-of-voice series you can export month over month, which is the shape this row needs. Log this month’s number and last month’s number in the same cell. A single snapshot means nothing without its predecessor.
Use the same prompt set you tracked last month. If you added or retired prompts this period, note it once in the caveat line rather than letting an invisible change explain away a number that actually moved.
Step 2: Split sentiment three ways instead of averaging it into one score
Do not compress sentiment into a single “brand health” number. Report the three-way split: positive, neutral, negative.
Most brands sit close to a neutral-heavy baseline. A February 2026 analysis of more than 1.8 million AI brand mentions by rocketblue (formerly Spotlight) found 80.6% were neutral, 18.4% positive, and just 1% negative. Plot your own split next to that line. A sentiment mix that leans meaningfully more negative than the benchmark is the number to flag first, not an average score that hides it.
Step 3: Break out coverage by engine, not as a blended average
List all five engines individually: ChatGPT, Perplexity, Google AI Overviews, Gemini, and Microsoft Copilot. Show a mention flag or a percentage for each one, even the engines where your brand has zero presence.
Peec AI’s per-engine dashboards are built for exactly this row: a side-by-side breakdown by model instead of one combined figure. If Google AI Overviews is the gap, say so by name. Semrush’s AI Overview tracking inside its AI Toolkit isolates that engine specifically, which helps when AI Overviews is the row your CMO cares about most, since it sits inside a Google result they already watch.
Step 4: Add AI referral traffic against the market benchmark
This is the row that connects visibility to the website. Pull it from your analytics platform, segmented by referrer, not from your AI monitoring tool: filter sessions where the referrer is an AI platform domain, and express that as a percentage of total sessions.
Plot it against the market line. AI platforms accounted for 0.32% of total website traffic in 2026, up from 0.24% in 2025 and 0.02% in 2024, according to SE Ranking’s 2026 traffic study. Most brands still sit under 1%. A rising trend line matters more than the absolute number, so report the trend first and the raw percentage second.
Step 5: Write the caveat line once, and reuse it every month
AI engines are non-deterministic. Two people asking the same question can get two different answers, and the same person can get a different answer tomorrow. A report that ignores this looks naive the first time someone tests it themselves.
Solve it with one sentence, unchanged month to month:
“These numbers reflect an average of five runs per prompt, collected between [start date] and [end date]. AI engines return different answers to different users; this report shows the pattern across runs, not a guarantee for any single conversation.”
Put it at the bottom of the page, once. Do not hedge every number in the body of the report with its own qualifier. State the method one time, then let the numbers stand.
Step 6: Turn the five metrics into one page and one slide
A CMO reads one page or one slide, not a fifteen-tab spreadsheet. Once the five rows above are filled in, format them two ways: the one-page report for anyone who wants detail, and the single slide for the meeting itself. Both are below.
Send it on the same day every month, ideally the first business day after your monitoring tool finishes its monthly refresh. A predictable cadence trains your CMO to expect the page, the same way they expect a board deck on the same schedule every quarter.
Two benchmark lines worth plotting
Two numbers from outside your own data belong on the scorecard every month, even when nothing else moved.
AI referral traffic: 0.32% of total website traffic in 2026. SE Ranking’s 2026 traffic study puts AI platforms at that share, up from 0.24% in 2025 and 0.02% in 2024. Treat it as a market floor, not a target. A brand sitting above 0.32% is ahead of the average site, not necessarily ahead of its own category.
Sentiment split: 80.6% neutral. rocketblue’s February 2026 analysis of more than 1.8 million AI brand mentions found 80.6% neutral, 18.4% positive, and 1% negative. Most brands land close to that split by default, since AI engines tend to describe products in factual terms rather than strong opinion language. A split drifting noticeably more negative than the benchmark, not the raw negative percentage alone, is the signal worth escalating.
Plot both lines on the scorecard every month. A flat trend against a moving benchmark is still information.
What good reporting looks like: an annotated one-page report mockup
Here is the report structure, filled in with example numbers, annotated section by section.
Headline (one sentence stating the direction of travel, not a metric dump)
“Share of voice held at 34% across five engines this month. Citation share rose from 11% to 15% on the strength of two new comparison pages.”
Scorecard (the five metrics, this month against last month and against a benchmark)
| Metric | This month | Last month | Benchmark |
|---|---|---|---|
| Share of voice | 34% | 33% | Top competitor: 41% |
| Citation share | 15% | 11% | Track your own trend; no market norm exists |
| Sentiment split | 79% neutral / 19% positive / 2% negative | 81% / 16% / 3% | Category norm: ~80.6% neutral (rocketblue, February 2026) |
| Engine coverage | 4 of 5 | 3 of 5 | Target: 5 of 5 |
| AI referral traffic | 0.41% of sessions | 0.36% of sessions | Market average: 0.32% (SE Ranking, 2026) |
Engine detail (one line per engine, so the gap is visible by name, not buried in an average)
ChatGPT: mentioned. Perplexity: mentioned. Google AI Overviews: mentioned. Gemini: mentioned. Microsoft Copilot: not mentioned this period.
Three actions for next month (tied directly to the gaps the scorecard just showed)
- Close the Copilot gap with a Bing-indexed comparison page.
- Publish one more citable reference page; citation share is the fastest-moving metric this quarter.
- Hold sentiment steady; no negative-mention source needs correction this period.
Caveat line (the sentence from Step 5, unchanged)
That is the whole page. Five rows, one headline, three actions, one caveat.
The copyable slide outline
Turn the same page into an eight-slide deck for the review meeting itself.
- Title slide: “AI Visibility: [Month] Report”
- Headline: the one-sentence direction of travel, large type, no chart
- Scorecard: the five-metric table, this month versus last month versus benchmark
- Engine breakdown: all five engines listed by name, with the gap called out
- Sentiment detail: the three-way split, plus one example excerpt of a neutral and a positive mention
- What moved and why: the one or two actions from last month that changed a number
- Next month’s actions: the three items tied to this month’s gaps
- Methodology footnote: the caveat line, run count, and date range
Copy that outline into your deck tool of choice and fill in the five metrics. Nothing on the list requires more than a table and a sentence.
The wider list of monitoring platforms, including Peec AI, Otterly.AI, and Semrush, is at /rankings/ai-visibility-tools. If you would rather not stitch four tools together to fill in the scorecard, Temso tracks share of voice, citation share, sentiment split, and engine coverage in one workspace from $89/mo; you would still pull AI referral traffic from your own analytics. Metric definitions for terms like citation share and share of voice are in the glossary, and the full scoring approach behind the benchmarks above is at /methodology.
Build this month’s report with the scorecard above, write the one-line headline first, and send the one-pager before your CMO asks for it.