AI ROI
Methods for connecting AI tooling spend and adoption with defensible engineering and business outcomes.
Start with attributable delivery outcomes, not licence activity. A credible ROI view states AI spend, the work included, the comparison baseline, engineering cost assumptions, and the quality or rework costs that reduce the gain. These guides separate operational evidence from hypothetical examples so leaders can see what the data supports.
Questions this topic helps answer
- What counts as a measurable return from an AI coding tool?
- How should rework, review effort, and unused licences affect the calculation?
- Which assumptions must be disclosed before an ROI figure is board-ready?
Each article identifies its author, publication and update dates, related reading, and the Tetriz capability connected to the subject. Quantitative claims should state their source and limits under the research methodology.
Articles about ai roi
How to measure AI coding tool ROI for engineering teams
Four signals measure AI coding tool ROI reliably: cycle time reduction, story-point velocity per FTE, deploy-to-fix latency, and code review time compression. Combined, they isolate whether AI tooling spend is producing engineering outcomes or just inflating activity dashboards.
The four categories of enterprise AI and why engineering needs its own
Enterprise AI categories split into four types, and most purchasing conversations conflate all of them. A CTO who cannot tell these apart will spend budget on a tool that delivers seat usage dashboards when what the board actually needs is session-level attribution.