AI Impact Metrics

As business leaders, we constantly strive to improve, and the right metrics can play a vital role in measuring our progress. With AI now being applied across multiple domains, understanding its true impact has become even more important.

We’ve gathered a set of metrics currently being used by different companies, and we’d love to hear from you:

Your input will help us build a clearer picture of how AI impact is measured across industries. Thank you!

Your Name

Your Company URL

What AI metrics should we track? (Select all that apply)

Developer Productivity & Workflow

AI time savings- Estimated hours or days saved per developer by using AI tools.

Active coding/investigation/review time per change

Innovation ratio (% time on feature development)- Proportion of time AI frees up for feature work vs maintenance.

Code Quality & Delivery

Number of bugs - Volume of bugs introduced or resolved with AI involvement.

% code coverage - Extent of code tested or verified when assisted by AI.

DORA metrics - DevOps performance indicators like lead time, MTTR, failure rate.

PR revert rate - How often AI-assisted changes are reverted after merging.

Diffs per engineer- Code changes or differences produced per developer using AI.

AI Usage & Adoption

% of code written by AI - Portion of production code drafted or submitted by AI.

% of PRs assisted by AI - Share of pull requests that involved AI contributions.

Acceptance rates of AI-generated suggestions - Percentage of AI code suggestions accepted by developers.

AI tool usage (DAU / WAU / MAU) - Daily, weekly, or monthly active users of AI tools.

AI model usage - Overall volume of AI model calls or executions.

AI spend (total and per developer) - Financial investment in AI tools overall and per employee

Developer Experience & Satisfaction

Developer satisfaction - Overall happiness and morale of developers using AI tools

Developer experience - Impact of AI on code quality, focus time, and satisfaction

Developer Experience Index (DXI) - Composite measure of developer productivity and well-being

Perceived productivity - Developers’ self-assessment of productivity due to AI tools.

Perceived friction - Developers’ sense of barriers or inefficiencies caused by AI.

AI tool customer satisfaction (CSAT) - Satisfaction scores specific to AI tools.

Usage Behavior & Licenses

Usage intensity - Frequency and depth of AI tool usage by developers.

Usage events - Specific AI interactions logged, e.g., completions or chat replies

Power users - Developers with consistently high AI tool usage.

Unused licenses - Number of AI tool licenses purchased but not actively used.