AI Measurement
Plan, Deploy, and Prove AI ROI, Continuously
Know what AI is delivering and where it's draining ROI so you can double down on what actually works.
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AI Strategy
Target AI where it matters
- Identify top SDLC areas to target your AI investments
- Flag dev tasks that can be automated with AI
- Track bottom-up use cases and efficacy
AI Code metrics
Track AI code from IDE to production
- Identify AI vs. human code locally & securely without source code ever leaving the machine
- Track % of AI code by commit, PR, and repo
- Works across all IDEs and AI tools
AI Spend optimization
Monitor costs and licenses
- Right-size AI spend through ROI metrics
- Cut AI tools that aren't delivering
- Identify unused licenses
AI enablement
Build a stronger foundation for AI
- Identify early adopters, champions, and teams that need support
- Use readiness scorecards to surface gaps in documentation and standards
- Track rollout progress and tie adoption milestones to productivity outcomes
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AI Impact analysis
Measure AI impact across the SDLC
- Correlate increased AI usage with productivity metrics
- Analyze longitudinal trends and before vs after
- Isolate impact down to specific tools and cohorts
AI workflow optimization
Use data to optimize AI dev workflows
- Identify where in the SDLC to deploy AI
- Collect real-time analytics and feedback
- Measure results to verify success
AI Vendor Evaluation
Run head-to-head evaluations
- A/B test the efficacy of different tools
- Align your vendor strategy with your data
- Right-size AI spend through ROI metrics
whitepaper
Measuring AI code assistants and agents
To thrive in the AI era, organizations must adapt quickly. The DX AI Measurement Framework™ is a research-based set of metrics for measuring the impact of AI-assisted engineering.
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