Assessing the impact of AI coding assistants: Survey template

AI coding assistants such as GitHub Copilot, Amazon CodeWhisperer, and Tabnine are rapidly reshaping software engineering. These tools promise to accelerate development, improve code quality, and enhance the developer experience. But engineering leaders face a critical challenge: how do you measure whether AI adoption is truly improving developer productivity across individuals, teams, and systems?

A ready-to-use survey for AI adoption

This survey template gives leaders a structured way to evaluate AI coding assistants before and after rollout. It helps capture:

• Individual productivity – the speed, effort, and perceived quality of work with AI tools
• Team experience – how AI improves collaboration, code readability, and maintainability
• System health – the impact of AI-assisted code on testing, reliability, and performance

By collecting this feedback on a regular cadence, organizations can measure adoption trends, track outcomes, and identify where productivity initiatives are succeeding.

How to connect surveys to the DX AI Measurement Framework

Surveys alone provide valuable sentiment data, but to fully understand AI’s business impact, they should be paired with the DX AI Measurement Framework. This framework enables leaders to go beyond usage metrics and quantify adoption, time savings, quality improvements, and business outcomes. Together, surveys and structured measurement give a complete view of how AI is influencing software engineering productivity.

Turning AI adoption into measurable productivity gains

With this template, leaders have a repeatable method for evaluating AI coding assistants across the entire engineering organization. Whether you’re rolling out Copilot, CodeWhisperer, Tabnine, or other GenAI tools, the combination of baseline surveys, ongoing measurement, and the DX AI framework ensures that adoption translates into real productivity gains and strategic impact.