Jellyfish Launches AI Impact Suite to Quantify Engineering ROI

The updated platform, introduced during the company's inaugural AI Impact Week, aggregates data from the entire engineering stack. By integrating signals from tools like GitHub Copilot, Cursor, and Claude Code, the system allows managers to differentiate between manual tasks, AI-assisted work, and fully autonomous agent activity. A core addition, Lifecycle Explorer, identifies bottlenecks by visualizing where developers spend time, helping teams avoid over-investing in code generation when review processes are already saturated.

Beyond basic adoption tracking, the platform introduces behavioral metrics to assess how human engineers collaborate with AI agents. This data helps identify which teams are successfully scaling their output and which remain stalled. Furthermore, Jellyfish addresses the complexity of AI expenditure by mapping token usage and subscription costs directly to specific projects and roadmap deliverables. This spend-to-work attribution provides a holistic view of R&D investment, enabling leaders to compare their efficiency against industry benchmarks derived from over 1,300 organizations.