[Home](https://gavek.dk/)/[AEO knowledge hub](https://gavek.dk/aeo/)/A monthly AI visibility report: decisions, evidence and uncertainty AEO · evidence and practical implementation # A monthly AI visibility report: decisions, evidence and uncertainty Use a reporting structure that separates delivery, sampled answers and business outcomes. The example is explicitly hypothetical; the downloadable register starts blank. ## Open with the decision State what the owner needs to decide this month: approve a factual correction, commission evidence, resolve an access issue or continue an existing test. Then show the evidence behind that choice. A report filled with percentages but no definition or action does not establish business value. Keep the approved goal visible without implying that a platform guarantees the desired outcome. ## Keep three evidence sections Delivery records list accepted changes, exact URLs, approvals and live verification. Visibility records show panel version, completed runs, mention and citation definitions, captures and limitations. Business records show authorised referral and qualified-enquiry observations. The sections may inform each other but should not be added into a single invented success score. ## Read the sample honestly The illustrative table uses a tiny fictional panel solely to show reporting mechanics. Its numbers are not Gavek results, a benchmark, statistical significance or a forecast. In a real report, show both counts and denominator, unchanged test conditions and exclusions. If no valid baseline exists, do not calculate a growth rate. If analytics is unavailable, use NOT MEASURED rather than zero. ## Close the learning loop Record alternative explanations, unresolved claims and tasks awaiting a specialist. Assign the next owner and review point. A content update should carry an actual change log; changing the review date alone is not progress. Keep quarterly methodology review and event-driven platform-documentation checks alongside the monthly discussion. **ILLUSTRATIVE SAMPLE — fictional values, not Gavek or customer results**A demonstration of reporting fields only | Metric | Illustrative baseline | Illustrative later sample | Limitation | | Completed eligible runs | 10 | 10 | Tiny fictional sample | | Brand mentions | 2 / 10 | 3 / 10 | No significance or causality claim | | Linked citations | 1 / 10 | 1 / 10 | Different from mentions | | Qualified enquiries | NOT MEASURED | NOT MEASURED | No connected business data | [Download the blank CSV worksheet](https://gavek.dk/assets/aeo-worksheets/monthly-report-en.csv) CSV opens in spreadsheet applications. Keep evidence locally or in your authorised workspace; no answers are submitted by this download. ## Continue with the related decision - [AEO learning paths](https://gavek.dk/aeo/) - [Correct inaccurate AI answers by tracing the underlying facts](https://gavek.dk/learn/correcting-inaccurate-ai-answers/) - [Discuss the scope of AEO and AI-search work](https://gavek.dk/services/ai-search/) ### Sources, method and review information Editorial publisher: Gavek. Reviewed 8 November 2026. Practical methods and clearly labelled examples are original editorial guidance, not customer results or a disclosed platform algorithm. Check sources when implementing. - [Google Search · google](https://developers.google.com/search/docs/appearance/ai-features) Your next move starts here ## Make search a more useful part of your growth. Start with your website. Leave with a clearer picture of what to improve, what to measure and where to focus next. [Discuss your SEO project ↗](https://gavek.dk/contact/)[Explore our services ↗](https://gavek.dk/services/)