Your AI coding spend is soaring, but can you prove its ROI? | Harness Resource

Enterprise spend on AI coding agents has roughly tripled in sixteen months, according to Gartner. By 2028, the average company’s AI coding costs will overtake the average developer’s monthly salary. Yet, when asked about productivity, leaders can’t just point to adoption numbers. It’s time to explain how each tool, on each specific workflow, is worth the token spend. That means pinpointing improvements in throughput, incident rate, and release cadence to show real progress toward business goals. In this on-demand panel, we will explore how to map developer coding tools — such as Cursor, GitHub Copilot, and Claude Code — to the delivery outcomes they actually move. This enables engineering leaders to walk into the next budget cycle with clear evidence of what’s worked and a plan for which of the next generation of coding agents to bring in. Key takeaways: How to tell which AI coding tools are actually earning their token spend How to build an evaluation framework for the next generation of coding agents How to bring your board a plan, not just a budget request