If Gemini Enterprise adoption metrics look wrong or are missing, first identify the exact app, dashboard metric, reporting period, time zone, and user population you are comparing. Then check data-store linkage and refresh timing, confirm what the metric actually counts, and verify that the relevant event capture and observability are enabled. A missing chart and a surprising value can have different causes.
Start by defining the discrepancy
Before changing configuration, write down the Gemini Enterprise app and project, the dashboard tab and metric, the reporting interval and time zone, and the users included. Also record what you are treating as the expected “truth”—for example, an internal user roster, application logs, or a custom report. Distinguish a chart with no value from a value that seems implausible; the checks differ.
Google’s Analytics view separates Adoption from Usage and Quality. Use the metric definition for the specific view rather than assuming every chart measures the same population. Google’s Gemini Enterprise Analytics documentation describes the available views and definitions.
Check linkage, refresh timing, and default values
Analytics app metrics are available only from the time a data store is linked to the app. If a data store was unlinked, some app metrics can be lost; for data encrypted with CMEK, Google says those specific metrics cannot be retrieved. A newly linked data store therefore does not establish that earlier metrics will appear retroactively.
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Analytics refreshes about every six hours, so activity or a deployment change made recently may not yet be reflected. If there are no search logs or user events, Google documents that default values are shown for all metrics. Check the linkage history and whether events exist before interpreting a flat or empty display as zero adoption. See the Analytics setup and refresh details.
Make sure “active users” means what you think it means
In Gemini Enterprise Analytics, total users means unique active users during the selected period. A user is active if they sign in and trigger any API call—including loading the home page—and each user is counted once even if they call multiple methods. It is therefore not a count of people who meaningfully used a particular feature.
Seats purchased, seats claimed, and active users answer different questions. The documentation describes purchased and claimed seats as cumulative since registration, while active users are period-based. Do not compare these figures as though they share a time window or measure. Google’s metric definitions are the appropriate reference when reconciling these counts.
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Check event capture for custom API clients
Searches and answers performed through the Gemini Enterprise UI do not require extra event configuration for Analytics. If your organization calls Gemini Enterprise APIs from its own applications, however, the application must configure search to capture and import user events for applicable analytics.
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- Search and view-item events are required to calculate click-through rate.
- Attribution metrics use only events that include the Google-generated attribution token in the resulting user event. Verify that the token is passed through and preserved.
- Google recommends the optional
SearchRequest.UserPseudoIdfor session-level analytics and search quality.
Missing or incomplete custom-client events can make a dashboard differ from application-side request counts without indicating that the dashboard is broken. Review the user-event requirements.
Match the telemetry layer to the question
App-scoped Engine metrics cover traffic to the app and are collected regardless of observability settings. Agent-scoped metrics—such as sessions, turns, and tool invocations—have a separate prerequisite: OpenTelemetry instrumentation must be enabled. For Core Assistant, enable it in the app configuration; for supported employee-made Workflow Builder and Deep Research agents, enable it in the individual agent configuration.
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These layers also differ in timing and retention. Metrics Explorer data refreshes about every minute, while Gemini Enterprise metrics with the discoveryengine.googleapis.com/ prefix are retained for six weeks by default, after which older data is deleted. A missing agent chart may therefore point to instrumentation, whereas missing app-level traffic should be investigated through app analytics and request data. Google documents metric scope and access and observability configuration.
Trace errors to a connector or access problem
A drop in apparent adoption can reflect failed requests rather than fewer attempts to use the product. For a data-store problem, follow Google’s troubleshooting sequence:
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- Review request and error trends for the relevant Engine or tool.
- Find a failing request in Trace Explorer.
- Inspect its error payload in Logs Explorer.
- If the error suggests a changed IAM policy, missing scope, or denied permission, cross-reference Cloud Audit Logs to identify relevant access changes.
Where appropriate, filter by Engine and Tool IDs so unrelated traffic does not obscure the pattern. This sequence can distinguish a connector or permission failure from a genuine decline in usage. See Google’s data-store troubleshooting guidance.
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Use audit logs with care
Gemini Enterprise usage audit logs require observability configuration and appropriate Gemini Enterprise Admin and Logs Viewer roles. Full prompt and response content logging is controlled separately. Because prompts and responses can contain sensitive information or personally identifiable information, restrict log access to authorized personnel and enable content logging only when justified by the investigation and your organization’s controls.
Google’s audit-log setup guide covers prerequisites and access considerations.
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The Analytics export API can export the past 30 days, including the current day, to BigQuery. Google documents limits of five calls per day per project and 25 per day per organization. Google Cloud’s July 15, 2026 article describes combining Gemini Enterprise log sinks with BigQuery for broader organization-centric analysis. See export documentation and Google Cloud’s BigQuery usage-analysis overview.
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Before treating a custom report as a correction to the product dashboard, document the differences that affect the result:
- What behavior counts as activity, and whether errors are included or filtered.
- Whether the report is app-scoped or agent-scoped.
- How identities are deduplicated, and which user events and attribution tokens are present.
- The reporting window, time zone, refresh cadence, and retention period.
- The precise metric definitions used by each view.
Do not assume that every chart counts requests the same way: Google notes, for example, that Engine request total latency includes requests returning errors, while other metrics have their own definitions. Treat comparisons between dashboards, exports, logs, and BigQuery as comparisons between measurement systems until their coverage and definitions are aligned. Metric definitions are documented here.
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