Engineering Enablement by Abi Noda

In this episode, Abi Noda speaks with Derek DeBellis, lead researcher at Google’s DORA team, about their latest report on generative AI’s impact on software productivity.

They dive into how the survey was built, what it reveals about developer time and “flow,” and the surprising gap between individual and team outcomes. Derek also shares practical advice for leaders on measuring AI impact and aligning metrics with organizational goals.

• LinkedIn: https://www.linkedin.com/in/derekdebellis/

• LinkedIn: https://www.linkedin.com/in/abinoda 

(00:00) Intro: DORA’s new Impact of Gen AI report

(03:24) The methodology used to put together the surveys DORA used for the report 

(06:44) An example of how a single word can throw off a question 

(07:59) How DORA measures flow 

(10:38) The two ways time was measured in the recent survey

(14:30) An overview of experiential surveying 

(16:14) Why DORA asks about time 

(19:50) Why Derek calls survey results ‘observational data’ 

(21:49) Interesting findings from the report 

(24:17) DORA’s definition of productivity 

(26:22) Why a 2.1% increase in individual productivity is significant 

(30:00) The report’s findings on decreased team delivery throughput and stability 

(32:40) Tips for measuring AI’s impact on productivity 

(38:20) Wrap up: understanding the data