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Despite headlines that AI is improving developer productivity, only 6% of engineering leaders have seen a significant productivity boost so far, according to a recent LeadDev survey.
As developers continue to adopt generative AI tools to help them with coding tasks, there’s been no shortage of bold claims being made about the impact on their productivity.
GitHub says 88% of Copilot users feel more productive when using the coding assistant. Jit’s director of engineering, Daniel Koch, says its engineering team has been as much as 3 times more productive when it comes to the time taken to deliver new features thanks to the Cursor coding assistant. And JPMorgan Chase’s global chief information officer, Lori Beer, told Reuters that its software engineers increased their productivity 10% to 20% by using a homegrown coding assistant tool.
However, only 6% of the 617 engineering leaders surveyed for LeadDev’s Engineering Leadership Report in March reported a significant boost in productivity.
Low hanging fruit
Since the launch of OpenAI’s ChatGPT in November 2022, generative AI tools like GitHub Copilot and Cursor have quickly become staples in many developers’ workflows.
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“AI came for code generation first because it was the easiest problem to solve,” says Charity Majors, CTO at the observability platform Honeycomb, told LeadDev in a recent interview.
AI tools are increasingly being embedded into daily coding routines – often directly within IDEs like VS Code or JetBrains – supporting tasks such as automated code generation for 47% of respondents, code refactoring (45%), and generating documentation (44%).
Their widespread adoption has fueled AI hype – even in boardrooms – about AI’s potential to significantly boost engineering productivity.
Despite the hype, 39% of respondents to LeadDev’s survey reported more minor productivity gains of between 1-10%.
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“AI-powered assistants offered developers an all-powerful co-pilot, generating code snippets on the fly. The industry buzzed with anticipation, and ‘AI Toolbox’ became a sought-after skill. Yet, as time has passed, developers have realized that AI is not a universal solution,” software developer Wesley Huber wrote.
Bridging the gap
What explains the disconnect between engineering leaders’ enthusiasm to implement AI and the lack of perceived productivity gains for developers?
This is because, in the view of Andrew Zigler, senior developer advocate at LinearB, there is a gap between the promise of AI and the practical realities of engineering workflows.
Most tools are geared toward writing code and this narrow focus overlooks deeper pain points that affect developer effectiveness.
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“We need to stop talking about AI as a magic fix and instead focus on the specifics: where are the biggest points of friction for developers, how can AI help alleviate that friction, and specifically how should developers use AI tools to overcome that friction and move faster?” Laura Tacho, CTO at DX said.
“In any process you want to look at where the largest bottlenecks are in the process and knock those down until you find the next largest bottleneck [and so on],” explained Rebecca Murphey, Field CTO of Swarmia.
Ask your devs
The buzz around AI frequently misses the mark by not truly understanding the day-to-day nature of engineering tasks.
For example, LeadDev’s report showed that AI is primarily applied for automated code generation, refactoring and documentation, but not critical day-to-day activities like bug fixing (22%) and internal team communication (28%).
“The bottlenecks that we tend to see at companies are not in the hands-on keyboard time; [it] is in the time waiting for the test to pass or fail, or for a build or deploy that won’t happen for another two to three days,” explained Murphey.
To address core challenges effectively, engineers need to be included in discussions with leadership about implementing AI tools. This collaboration helps determine the appropriate use cases for AI within the organization and the specific problems it should aim to solve.
“Too many AI tools are brought in through top-down enthusiasm rather than bottom-up validation. If the people writing the code aren’t part of the decision, you risk solving for the wrong problem or introducing new ones.” Zigler said.
“It is making sure everybody is starting from problem identification not solution suggestion which is what AI has turned into,” Murphey added.
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Tacho added that “to see org-wide change, it needs to be an org-wide effort – not just throwing licenses at your developers and hoping that some individuals figure out how to best use the technology.”
Ultimately, for AI to deliver real value in engineering, it must be grounded in the lived reality of those closest to the work – because meaningful progress starts not with hype, but with listening.
To read more about how engineering leadership is changing in response to widespread AI disruption, dig into the full Engineering Leadership Report.
