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AI adoption survey

Why it matters​

Usage telemetry (active seats, invocation counts, suggestion acceptance rate) is important for knowing how much AI tools are being used. But it can't tell you whether people believe the tools are actually helping, and a developer can generate hundreds of suggestions while still spending more time reviewing and fixing them than they saved. Qualitative feedback closes that gap: asking developers directly whether AI is making them faster, saving time, improving quality, and freeing them for higher-value work.

What to track​

  • Adoption & reach: How often people use AI tools, and how broadly across their day-to-day work (coding, testing, reviews, docs, debugging).
  • Velocity: Whether AI tools are perceived to speed up individual tasks and team delivery.
  • Time & toil saved: How many hours a week AI reclaims from repetitive, manual work.
  • Quality & rework: Whether output quality has improved, and how much extra review or rework AI-generated work creates.
  • Focus & business impact: Whether AI frees people for higher-value work and speeds delivery of value to customers.

How Port helps​

Port's Survey Intelligence plugins include an AI Adoption framework alongside SPACE, DORA, and DX Core 4. Survey Builder generates a recurring "AI Adoption & Impact Survey" from this built-in template: five dimensions and thirteen questions, combining frequency questions, five-point agreement scales, a multi-select question on tool usage, and free text. The template is editable, not fixed: change its dimensions and questions, or start from a blank custom framework, and Survey Analytics scores it the same way as any built-in one. Responses are stored in the same context lake as your usage telemetry, so you can correlate survey scores directly with delivery metrics such as DORA metrics and cycle time.

Example scenario​

A platform team has been rolling out Claude Code and Copilot for two quarters. Usage dashboards show adoption is broad, but leadership wants to know if it's translating into perceived value. The team runs the built-in AI Adoption survey quarterly. Results show high scores on Adoption and Velocity, but a low score on Quality & rework, and the open-text responses reveal developers are spending real time correcting AI-generated test code. The team prioritizes better prompting guidelines and a review checklist for AI-authored tests, rather than pushing adoption further.

Example survey​

Survey Builder with an AI Adoption & Impact Survey open, showing the Adoption & reach and Velocity dimensions

Built from the AI Adoption framework template in Create Survey Intelligence.

Implement this use case

Follow the recommended guides below to implement this use case.