This chapter discusses different methods for measuring AI's impact on developer productivity and emphasizes the importance of setting realistic expectations and providing adequate training when implementing an AI tool. The speaker shares examples of surveys, controlled experiments, and telemetry analysis as methods for assessing productivity improvements. They also highlight the need for tailoring the tool to different companies and teams.

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