AI Control Tower: Source Tables for Custom Reporting and Performance AnalyticsIssue <!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } span { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } h2 { font-size: 24pt; font-family: Lato; color: var(--now-color--text-primary, black); } h3 { font-size: 18pt; font-family: Lato; color: var(--now-color--text-primary, black); } h4 { font-size: 14pt; font-family: Lato; color: var(--now-color--text-primary, black); } a { font-size: 12pt; font-family: Lato; color: var(--now-color--link-primary, #00718F); } a:hover { font-size: 12pt; color: var(--now-color--link-primary, #024F69); } a:target { font-size: 12pt; color: var(--now-color--link-primary, #032D42); } a:visited { font-size: 12pt; color: var(--now-color--link-primary, #00718f); } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } AI Control Tower (AICT) builds its built-in visualizations — the Value Dashboard, AI Asset Inventory, and AI Gateway metrics — by reading from a set of internal system tables. Administrators and developers who want to build their own reports, Performance Analytics indicators, or custom dashboards using the same underlying data can point their configurations at the tables described in this article. This article is a reference for read-only reporting use. It does not document how to configure AI Control Tower itself. Release<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } span { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } h2 { font-size: 24pt; font-family: Lato; color: var(--now-color--text-primary, black); } h3 { font-size: 18pt; font-family: Lato; color: var(--now-color--text-primary, black); } h4 { font-size: 14pt; font-family: Lato; color: var(--now-color--text-primary, black); } a { font-size: 12pt; font-family: Lato; color: var(--now-color--link-primary, #00718F); } a:hover { font-size: 12pt; color: var(--now-color--link-primary, #024F69); } a:target { font-size: 12pt; color: var(--now-color--link-primary, #032D42); } a:visited { font-size: 12pt; color: var(--now-color--link-primary, #00718f); } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } All Resolution<!-- /*NS Branding Styles*/ --> .ns-kb-css-body-editor-container { p { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } span { font-size: 12pt; font-family: Lato; color: var(--now-color--text-primary, #000000); } h2 { font-size: 24pt; font-family: Lato; color: var(--now-color--text-primary, black); } h3 { font-size: 18pt; font-family: Lato; color: var(--now-color--text-primary, black); } h4 { font-size: 14pt; font-family: Lato; color: var(--now-color--text-primary, black); } a { font-size: 12pt; font-family: Lato; color: var(--now-color--link-primary, #00718F); } a:hover { font-size: 12pt; color: var(--now-color--link-primary, #024F69); } a:target { font-size: 12pt; color: var(--now-color--link-primary, #032D42); } a:visited { font-size: 12pt; color: var(--now-color--link-primary, #00718f); } ul { font-size: 12pt; font-family: Lato; } li { font-size: 12pt; font-family: Lato; } img { display: ; max-width: ; width: ; height: ; } } Value Dashboard The Value Dashboard (AI Control Tower > Home > Insights > Value) surfaces productivity metrics including hours saved, net AI returns, and value breakdown by AI system. It draws from several layers of tables. Calculated Value and Extended Metrics sn_value_calculated_data_v2 — Stores the final calculated value output per asset-to-template mapping. This is the primary table backing the summary figures displayed on the dashboard.sn_value_extended_metrics_v2 — Stores extended usage metrics per mapping, including: execution_task_usage, execution_task_successful_usage, standalone_usage, standalone_successful_usage, skill_usage_by_capability, total_usage, and total_successful_usage. Value Template Definition Tables sn_value_template_v2 — Defines individual Value Templates (the calculation configurations that determine how value is measured for a given AI system or skill).sn_value_template_mapping — Maps a Value Template to a specific AI system or asset.sn_value_template_rule — Stores the calculation rules that belong to a template. Creator Skills Sub-area sn_value_creator_metrics — Metrics specific to Creator Skills usage within the Value Dashboard.sn_value_task_using_skills — Records tasks that were completed using Creator Skills. Upstream Usage Tables The calculated value figures on the dashboard are built on top of Performance Analytics daily indicators. Those indicators are sourced from the following raw usage tables. Administrators building a custom PA Indicator Source can point directly at these tables rather than implementing separate usage tracking. sys_gen_ai_usage_log — Daily execution counts and assist averages for native ServiceNow skills and agents. Backs PA indicators including AIValue - Daily Agent Executions, AIValue - Daily Skill Executions, AIValue - Daily Agent Workflow Executions, AIValue - Daily Average Skill/Agent Workflow/Agent Assist, and AIValue Average RWTS per Skill.sn_ai_disc_ai_usage — Usage data for third-party and external agent invocations. Backs PA indicators in the AIValue.CreatorMetrics family, including AIValue.CreatorMetrics.AcceptedCalls. Engagement and Department Breakdown The department-level and country-level breakdowns in the Value Dashboard are derived at query time from user record data: sys_user.department — Used to group value data by department. Records where this field is empty are grouped into an "Others" category.sys_user (country field) — Used for country-level breakdowns.sys_choice — Provides display label resolution for department and country choice fields. AI Asset Inventory AICT's AI Asset Inventory backs list views and widgets across the application. The inventory is organized across two layers: digital asset records (one per tracked AI asset) and product model records (schema templates), with CMDB configuration item records created via the Identification and Reconciliation Engine. Digital Asset Tables ai_digital_asset — Parent table for all AI digital assets. Type-specific records extend this table.ai_model_digital_asset — AI model assets.ai_prompt_digital_asset — AI prompt assets.ai_dataset_digital_asset — AI dataset assets.ai_system_digital_asset — AI system assets. Product Model Tables Product model records define the schema and metadata structure for each asset type. ai_model_product_modelai_prompt_product_modelai_dataset_product_modelai_system_component_product_model CMDB Configuration Item Tables When an AI asset is ingested through AICT's discovery or intake workflows, corresponding CMDB CI records are created or updated via the Identification and Reconciliation Engine. cmdb_ci_ai_function — CI record for an AI function.cmdb_ci_ai_model — CI record for an AI model.cmdb_rel_ci_asset — Many-to-many relationship table linking CI records to asset records. AI Gateway The AI Gateway tab in AICT displays transaction volume, success rates, and failure metrics for MCP server interactions. This telemetry data is collected through a separate infrastructure layer and is not stored in a standard ServiceNow Glide table. Because this data does not reside in a Glide table, it cannot be queried through the Table API, used as a Performance Analytics Indicator Source, or targeted by a standard ServiceNow report data source. Custom reporting against AI Gateway metrics is not supported through the same mechanisms described for the Value Dashboard and AI Asset Inventory sections above. Important Notes These are internal system tables. The tables listed in this article — prefixed sn_, sys_, ai_, or cmdb_ — are intended primarily to support AICT's own out-of-box visualizations. They are documented here for read-only reporting purposes, such as pointing a Performance Analytics Indicator Source or a custom report at the same data AICT already reads. ServiceNow does not recommend modifying records in these tables outside of AICT's own application logic, as doing so may cause AICT's own calculations or dashboards to produce inconsistent results. Table schemas may change across releases. The specific tables AICT uses, and the fields within them, may change as the application evolves. New tables may be introduced and existing ones may be renamed or deprecated. This has already occurred: AICT's Value Realisation feature migrated from an earlier schema (sn_value_template / sn_value_template_applied) to the current _v2 tables in a past release. Administrators building custom reports against these tables should verify that the relevant tables and fields are still present on their instance after upgrades, particularly following major release family transitions.