How to measure GenAI adoption and impact

GenAI is all the hype right now. From boardrooms to newsrooms, the narrative centers around the revolutionary impact of GenAI tools such as GitHub Copilot on developer productivity and efficiency.

Yet, despite the widespread enthusiasm, there are a lot of challenges as well. Chief among them is determining the tangible impact of GenAI on developer productivity. Leaders need this information to validate and inform their investments. However, developer productivity has always been a complex problem, and measuring the impact of GenAI is no different.

At the same time, some organizations are seeing suboptimal developer adoption they find difficult to explain, and are looking to better understand why this is happening and how to address it. This problem is especially painful for leaders responsible for rolling out these tools—these people are doing so without feedback loops or insights into what the best use cases are and where developers are seeing the biggest gains.

Across these challenges, we see that there’s a common thread: it’s difficult to get useful feedback, signals, and measurements on how GenAI is impacting developer productivity.

At DX, we’ve been working with a number of organizations to solve these challenges, and are seeing promising results. In this article, we will share our learnings on the different approaches organizations are using, and provide guidance into how to combine the methods available into a holistic approach that gives organizations adopting GenAI tools the insights they need.