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Anthropic announced today it is rolling out a comprehensive analytics dashboard for its Claude Code AI programming assistant, addressing one of the most pressing concerns for enterprise technology leaders: understanding whether their investments in AI coding tools are actually paying off.
The new dashboard will provide engineering managers with detailed metrics on how their teams use Claude Code, including lines of code generated by AI, tool acceptance rates, user activity breakdowns, and cost tracking per developer. The feature comes as companies increasingly demand concrete data to justify their AI spending amid a broader enterprise push to measure artificial intelligence’s return on investment.
“When you’re overseeing a big engineering team, you want to know what everyone’s doing, and that can be very difficult,” said Adam Wolff, who manages Anthropic’s Claude Code team and previously served as head of engineering at Robinhood. “It’s hard to measure, and we’ve seen some startups in this space trying to address this, but it’s valuable to gain insights into how people are using the tools that you give them.”
The dashboard addresses a fundamental challenge facing technology executives: As AI-powered development tools become standard in software engineering, managers lack visibility into which teams and individuals are benefiting most from these expensive premium tools. Claude Code pricing starts at $17 per month for individual developers, with enterprise plans reaching significantly higher price points.
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A screenshot of Anthropic’s new analytics dashboard for Claude Code shows usage metrics, spending data and individual developer activity for a team of engineers over a one-month period. (Credit: Anthropic)
Companies demand proof their AI coding investments are working
This marks one of Anthropic’s most requested features from enterprise customers, signaling broader enterprise appetite for AI accountability tools. The dashboard will track commits, pull requests, and provide detailed breakdowns of activity by user and cost — data that engineering leaders say is crucial for understanding how AI is changing development workflows.
“Different customers actually want to do different things with that cost,” Wolff explained. “Some were like, hey, I want to spend as much as I can on these AI enablement tools because they see it as a multiplier. Some obviously are sensibly looking to make sure that they don’t blow out their spend.”
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