💻AI Coding Dashboard
Measure AI adoption, developer productivity, and subscription ROI for AI coding assistants across your engineering team.
The AI Coding Dashboard helps engineering leaders understand how their team is adopting AI-assisted development. Track usage patterns across multiple AI coding tools, identify productivity opportunities, and ensure your subscriptions are delivering value—all from a single view designed for measuring AI adoption at scale.
Overview
You've invested in AI coding assistant subscriptions for your team. But are developers actually using them? Who's getting the most value? Are there adoption gaps you should address?
The AI Coding Dashboard answers these questions:
Who's using AI coding assistants? See adoption rates across your team
How deeply are they using them? Measure session frequency, duration, and complexity
Which tools are being used? Compare adoption across Claude Code, Gemini CLI, and other assistants
Where is AI making an impact? Understand which workflows benefit most
Are we getting value from our subscriptions? Benchmark usage against subscription costs
Unlike usage-based AI services, AI coding assistants are typically fixed subscription costs. The goal isn't minimizing usage—it's maximizing adoption and ensuring your team gets full value from the investment.
Supported AI Coding Assistants
The AI Coding Dashboard currently supports:
Claude Code
Anthropic's AI coding assistant
Gemini CLI
Google's command-line AI coding assistant
Additional AI coding assistants will be added as integrations become available.
Dashboard Views
Organization vs. User View
Toggle between two perspectives using the view selector at the top of the dashboard:
User View: See metrics broken down by individual developers (default)
Organization View: Aggregate metrics across your entire organization for executive-level reporting
Organization View is useful for comparing AI adoption across teams, departments, or business units without drilling into individual user data.
Adoption Overview
Monitor how AI-assisted development is spreading across your organization:
Active Users: Count of developers using AI coding assistants in the selected period
Adoption Rate: Percentage of licensed users who are actively using the tools
New Users: Developers who started using AI assistants recently
Usage Frequency: How often each developer engages with AI coding tools
Spot adoption gaps early. If certain teams or individuals aren't using their subscriptions, you can provide training, share best practices, or reallocate licenses.
Usage Patterns
Understand how your team works with AI coding assistants:
Sessions per User: Average number of AI conversations per developer
Session Depth: Token consumption as a proxy for conversation complexity
Peak Activity Times: When does your team rely on AI assistance most?
Workflow Distribution: Code generation, debugging, code review, documentation, etc.
Tool Distribution: Usage split across Claude Code, Gemini CLI, and other supported tools
These patterns help you understand where AI is adding the most value and identify opportunities to expand usage to other workflows.
User Leaderboard
See which developers are getting the most from their AI coding subscriptions:
Top Users by Sessions: Most active AI coding assistant users
Top Users by Tokens: Developers with the deepest AI-assisted workflows
Usage Trends: Individual adoption trajectories over time
Team Comparisons: Compare adoption across teams or departments
High usage often correlates with productivity gains. Consider pairing power users with teammates who are still ramping up on AI-assisted development.
Model Usage
Track which AI models your team uses:
Model Distribution: Usage breakdown by model (Sonnet, Opus, Haiku, Gemini Pro, etc.)
Model by Workflow: Which models are used for which types of tasks
Model Trends: How model preferences evolve over time
This helps you understand whether your team is using the right models for their tasks and can inform decisions about which model tiers to include in subscriptions.
Measuring Subscription Value
Are We Getting ROI?
For fixed-cost subscriptions, the value equation is simple: more usage = better ROI.
The dashboard helps you calculate value by showing:
Cost per Active User: Subscription cost divided by developers actually using the tools
Usage Intensity: Tokens and sessions per subscription dollar
Adoption Trajectory: Is usage growing, flat, or declining?
If half your licensed users never open their AI coding assistant, you're paying double the effective per-user cost. The dashboard surfaces these gaps so you can address them.
Identifying Underutilization
Watch for warning signs:
Zero-session users: Licensed developers with no activity
Declining usage: Users whose engagement is dropping off
Shallow sessions: Very low token counts may indicate limited adoption
These signals help you target training, share success stories, or right-size your license count.
Key Metrics
Active Users
Developers who used AI coding assistants in the selected period
Adoption Rate
Active users as a percentage of total licensed users
Total Sessions
Number of AI conversations across all users
Sessions/User
Average sessions per active developer
Total Tokens
Combined input and output tokens processed
Tokens/Session
Average conversation depth
Session Count
Number of coding sessions (Organization View)
Getting Started
Prerequisites
To use the AI Coding Dashboard, you need:
Revenium SDK Integration: Your AI coding assistant usage must be metered through Revenium's SDK
User Identification: Configure your integration to pass user identifiers for attribution
Session Tracking: Enable session-level tracking for conversation analytics
Connecting Claude Code
Install the Revenium Claude Code metering package:
Package documentation: @revenium/claude-code-metering on npm
The package integrates with Claude Code to automatically capture usage telemetry and send it to Revenium for analysis. See the npm package README for configuration options and setup instructions.
Connecting Gemini CLI
Configure your Gemini CLI integration using Revenium's OpenTelemetry-compatible metering. See the Integration Options page for setup instructions.
Filtering and Analysis
Time Range Options
Last 24 hours
Last 7 days
Last 30 days
Last 90 days
Last 6 months
Last 12 months
Custom date range
Custom Date Ranges: Use the date picker to select any arbitrary date range for historical analysis or period-over-period comparisons.
Filter Dimensions
Slice AI coding data by:
User: Individual developers or teams
Team/Department: Compare adoption across groups
Tool: Filter to specific AI coding assistants (Claude Code, Gemini CLI)
Model: Specific model versions
Project: If project metadata is passed via SDK
Best Practices
Track Adoption, Not Just Usage
Focus on metrics that indicate healthy adoption:
Is the number of active users growing?
Are new hires onboarding with AI coding assistants?
Are power users emerging across different teams?
Share Success Stories
When you identify power users:
Learn what workflows they're using AI assistants for
Share their techniques with the broader team
Consider having them lead internal AI adoption sessions
Right-Size Your Licenses
Use the dashboard to inform licensing decisions:
If adoption is low, invest in training before buying more seats
If everyone is active and hitting limits, consider expanding
Reallocate unused licenses to teams showing interest
Set Adoption Goals
Combine the dashboard with Cost & Performance Alerts to:
Get notified when adoption drops below target thresholds
Alert when new users haven't engaged within their first week
Track progress toward team-wide adoption goals
Summary
The AI Coding Dashboard shifts the conversation from "how much are we spending?" to "are we getting value from our investment?" By measuring adoption, usage depth, and user engagement across multiple AI coding tools, engineering leaders can ensure their subscriptions translate into real productivity gains—and identify opportunities to help more developers benefit from AI-assisted development.
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