Enterprise AI Measurement Guide
Team Performance
Cost per Output
Is our AI coding tool investment making our engineering team more cost-efficient over time, or are costs rising as fast as, or faster than, the productivity gains?
What it shows
Cost per Output divides total AI tool spend by engineering output, producing a dollar cost per unit of complexity-adjusted engineering value produced. The trend chart shows whether this efficiency ratio is improving (cost per output falling as either productivity rises or spend stabilizes) or deteriorating (costs rising faster than output). It is the team-level expression of the ROI question: are we getting more efficient as AI investment grows?
Why it matters
AI tool spend rising alongside Output per Engineer rising tells an encouraging story. AI tool spend rising alongside flat Output per Engineer tells a different one. Cost per Output is the ratio that resolves the ambiguity. It shows whether the productivity gains are keeping pace with the cost increases, and whether the engineering org is building genuine AI-driven efficiency or just spending more on tools that produce proportionate cost alongside proportionate output. This is the metric that should anchor every AI coding budget renewal conversation.
The Larridin angle
Cost per Output is the only metric in the dashboard that puts spend and productivity in the same denominator, making it a direct expression of ROI at the team level, updated weekly, without requiring a separate analysis project to calculate.