How to measure AI developer productivity in 2025 | Nicole Forsgren

Oct 19, 2025 1h 7m 17 insights Episode Page ↗
Nicole Forsgren, creator of Dora and Space frameworks, discusses how AI impacts developer productivity and experience (DevEx). She shares insights on measuring gains, common mistakes, and a seven-step process to reduce friction and improve engineering team performance in the age of AI.
Actionable Insights

1. Conduct Developer Listening Tour

Before implementing any tools or automation, talk to developers and listen to their daily experiences. Ask them to walk you through their day, identifying delightful points, difficulties, frustrations, slowdowns, and friction to uncover high-impact improvement areas.

2. Prioritize Process Improvement

Look for unnecessarily complex or slow processes within your team or organization. Often, simple changes, like replacing a multi-step manual approval with an email, can significantly reduce friction without requiring engineering effort.

3. Rethink Workflows with AI

Structure your days and work to leverage AI’s ability to handle context and generate diagrams, potentially making shorter work blocks (e.g., 45 minutes) more useful for deep work by offloading flow initiation to the machine.

4. Plan AI-Assisted Development Upfront

When using AI agents for coding, spend more time upfront planning the architectural components, stack, and general workflow. This systematic approach allows AI to work in parallel on pieces, leading to more production-ready code and better outcomes.

5. Evaluate AI-Generated Code Critically

Do not blindly accept AI-generated code. Actively evaluate it for hallucinations, reliability, and adherence to typical style and conventions, as AI models are non-deterministic and can introduce complexity or technical debt.

6. Measure AI Impact Strategically

Tailor your AI productivity measurement to what your leadership cares about most (e.g., market share, profit margin, velocity). Frame your metrics in terms of their priorities, such as tracking ‘idea to customer’ speed for market share or cloud cost savings for profit margin.

7. Use Surveys for DevEx Baseline

If you’re just starting to measure developer experience, use well-designed surveys to quickly get an overall view of challenges. Ask specific, single-focus questions, have developers prioritize their top three issues, and inquire about the frequency of impact.

8. Focus on Developer Satisfaction

Instead of broad ‘happiness’ surveys, measure developer satisfaction with specific tools, processes, and their job. High satisfaction contributes to better work and team collaboration, which is a more actionable metric.

9. Clean Up Test and Build Suites

Address breaking builds and flaky tests, as these are clear signs of friction. Cleaning up test and build suites can save significant developer time, reduce toil, and lower cloud costs by eliminating wasted compute cycles.

10. Provide Organizational Support for DevEx

As a business leader, provide structure and support for DevEx initiatives. Communicate priorities, celebrate wins, and ensure these efforts are not isolated projects, as they have huge potential returns for the business.

11. Improve Documentation for AI Tools

Actively work on writing and cleaning up documentation and code comments. AI agents rely on good data for training and grounding, so better documentation leads to better performance from your AI tools.

12. Bring Product Mindset to DevEx

Approach DevEx improvements and metrics with a product mindset. Identify problems for users, create MVPs, iterate rapidly with feedback, define a clear strategy, understand your ‘market,’ and establish success metrics and communication plans.

13. Leverage AI for Strategic Refinement

Utilize AI tools to refine your product strategy, messaging, experimentation methods, and total addressable market analysis. This allows for more informed discussions with key stakeholders and faster progress.

14. Partner with Data Science for Experiments

Before running large-scale A/B tests or experiments, partner with your data science team. Use AI to inform initial plans, then consult experts to ensure proper instrumentation, avoid privacy/security issues, and guarantee usable data.

15. Recognize Signs of Team Friction

Be alert for signs that your engineering team could move faster, such as constant complaints about breaking builds, flaky tests, overly long processes, difficulty switching tasks or projects, or general dissatisfaction with the system.

16. Use AI for Home Design Visualization

Employ AI tools like ChatGPT or Gemini to render home design ideas. Provide floor plans, existing room photos, and images of desired items, then prompt the AI to change elements like walls or furniture layout to visualize concepts.

17. Explore Cloud Code for Non-Coding Tasks

Investigate Cloud Code for various non-engineering use cases, such as cleaning up laptop storage. It can act as a personal assistant, performing diverse tasks on your computer beyond just code generation.