The productivity paradox is real
AI coding assistants like Cursor, GitHub Copilot, and Claude Code deliver measurable gains in code generation speed: 3-15% less active coding time and 30-40% fewer context switches, according to telemetry from 10,000+ developers tracked by Faros AI.
The problem: the bottleneck has moved.
What actually slows developers down
The constraint in enterprise software delivery isn't typing speed. It's context reconstruction.
API contracts live in one system. Authentication rules in another. Business workflows exist as diagrams in Confluence, mixed with outdated versions across different projects. Legacy codebases. Slack threads. Zoom transcripts. Developers spend more time gathering scattered context than writing code.
This fragmentation compounds with every new service, team dependency, and integration point. AI tools can't solve what they can't see.
The metrics that matter
Smart CTOs measure AI coding tools through DORA metrics, not lines of code:
- Deployment frequency: up 10-25%
- Lead time: down 15-25%
- Recovery time: 10-20% faster
These improvements come from reduced context switching and faster debugging, not raw coding velocity.
The cost-benefit equation
Enterprise AI coding infrastructure runs $50k-$200k annually for API usage and tooling, plus $50k-$150k integration costs. For a team of 20 developers at 20% productivity gain, the math works: roughly $600k in annual savings.
Faros claims 15,324% potential ROI. Intercom reported 41% time savings after doubling adoption.
The knowledge retention problem
One data point worth watching: Anthropic's study showed AI-assisted developers scored 17% lower on Python library mastery tests (50% vs. 67% for traditional learners). Heavy reliance on AI can skip the debugging practice that builds deeper skills.
The trade-off matters for teams building complex, long-lived systems where maintainability trumps initial velocity.
What this means in practice
AI coding tools work when the context is already clear. They amplify good documentation and well-structured systems. They don't fix fragmented knowledge or unclear requirements.
Before expanding AI coding tool deployments, audit where your developers actually spend time. If it's reconstructing context, fix the documentation problem first. The AI will be more useful once it has something coherent to work with.