1. Establish adoption
Start with active users, tool coverage and the selected DAU, WAU or MAU threshold. Supported integration documentation includes GitHub Copilot, Cursor, Claude Code, Windsurf and Devin; feature availability and beta status vary.
AI
Measure GenAI’s Impact
See how AI is influencing engineering trends in your organization. Analyze usage patterns, adoption rates, and performance changes to optimize AI-driven development.
Measure AI usage and outcomes separately
Compare supported AI coding tools with adoption, review, delivery and quality signals.
Realize the complete impact of AI
Track adoption across supported AI coding tools, including GitHub Copilot, Cursor, Claude Code, Windsurf and Devin, and identify usage patterns, spot areas of low usage, and understand where GenAI is driving real value—or where it’s falling short.
Understand how GenAI is performing
Track key metrics like churn, cycle time, coding time, and review time to evaluate GenAI’s impact on development speed and efficiency. Compare team performance before and after adoption to understand where AI is accelerating delivery—and where it’s not.
Identify Areas of High and Low AI Leverage
Zoom into the languages, workflows, and teams where GenAI tools are delivering the most value. Identify inefficiencies and underutilized opportunities to maximize ROI from AI-powered development.
How it works · Data sources · Practical limitations
Measure AI coding adoption, delivery impact and ROI separately
AI usage is not the same as improved engineering outcomes. Waydev connects supported AI coding integrations to engineering data so leaders can investigate adoption, code-to-production signals and costs without attributing every delivery change to an assistant.
2. Compare engineering impact
Review AI-assisted changes alongside PR review time, cycle time, rework and code-to-production signals. Compare similar work and periods before interpreting correlation as causation.
3. Calculate net AI ROI
Use token usage and vendor spend where supported. Translate verified recovered engineering time into potential capacity value, then deduct AI tooling, rollout and support costs.
Example evaluation
An engineering VP asks whether Cursor adoption increased review demand across three product teams. The evaluation compares AI activity, merged work, review waiting and rework over comparable periods rather than reporting only an adoption percentage.
Interpretation and limitations: The availability, completeness and attribution of AI vendor data depend on the connected integrations. Not every AI-generated line can be reliably linked to business value.
Frequently asked questions
What is the difference between AI Adoption, AI Impact and AI ROI?
Adoption describes who uses AI and how frequently. Impact investigates engineering outcomes and checkpoints. ROI compares the measured value with AI tool and operating costs.
Does Waydev prove that AI caused an improvement?
No attribution should be assumed from simple before-and-after trends. Validate against work mix, team changes and quality indicators.
See Waydev in action: AI coding impact
Static product screenshots sourced exclusively from Waydev feature pages. Example interface data. AI-specific claims and outcomes should be evaluated against separate supporting evidence.
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