AI coding tools are finally delivering results for enterprises – developers are saving so much time they’re able to collaborate more, focus on system design, and learn new languages
Research from GitHub shows AI coding tools are hitting the mainstream, with developers reporting significant productivity and efficiency gains
![Software developer using AI coding tools on a desktop computer in an open plan office space.](https://cdn.mos.cms.futurecdn.net/m8LD3ENwxn3C9qP6jjPzb3-320-80.jpg)
Software development teams are beginning to see the value of AI coding tools, according to new research, with developers revealing the solutions have helped them learn new languages and save time.
GitHub surveyed 2,000 software engineers, developers, and programmers from the US, Brazil, Germany, and India, as well as a small number of data scientists and software designers.
Over 97% of respondents said they had used AI coding tools at work at some point, although some of these instances were not sanctioned by their companies.
GitHub noted that this year software development teams recognized more benefits with AI coding tools than previously reported. Developers using these solutions noted they were delivering more secure software, better code quality, improved test case generation, and faster adoption of new languages.
For example, 90% of the US respondents and 81% of those in India reported a perceived improvement in code quality when using AI tools, with 61% of those in Brazil and 60% in Germany stating the same.
There was virtually universal agreement that AI-enhanced coding tools would generate an overall improvement in code security across the board.
In addition, the majority of development teams expressed optimism about the potential of these tools to moderately improve or significantly enhance their ability to meet customer requirements
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Although the extent of this optimism was associated with the stance of the respondent’s firm on AI use in software development.
The level of optimism developers had for how AI tools could improve their workflow was linked to their organization’s stance towards AI more generally, however. GitHub found staff from firms that were actively encouraging AI adoption were more likely to express confidence in the technology, the results showed.
This suggests firms that are more supportive of AI could help their employees maximize the potential value they can unlock using the new technology.
Time savings, in particular, are helping development teams focus their energies elsewhere, GitHub found, with 47% of respondents in the US and Germany stating they used the saved time for collaboration and system design.
Productivity gains and time savings to be had with AI coding tools
GitHub’s findings corroborate similar investigations looking into how AI is shaping the development process. Stack Overflow’s 2024 Developer Survey found there is increased appetite for AI coding tools this year.
76% of respondents said they are using or planning on using AI tools in their development process, up from 70% in 2023.
Almost three-quarters of respondents (72%) were favorable or very favorable toward the use of AI in code development, with 81% agreeing productivity is the biggest benefit to AI tooling. Speeding up learning was identified as another major benefit for developers learning to code (71%) as well as established professional developers too (61%).
Speaking to ITPro, Angus Allan, senior product manager at CreateFuture (formerly xDesign), said GitHub’s findings align with his company’s experiences, describing the productivity gains from adopting AI coding tools as “substantial”.
“In our internal experiments, we observed a 33% average increase in productivity across various roles in the software development life cycle. A similar productivity increase was observed by our product team in non-coding activities, indicating that generative AI tools have broad applicability for teams building software products,” he explained.
“With our findings aligning approximately with earlier studies, it has given us confidence that GenAI has a real, tangible impact on productivity if harnessed correctly.”
Allan argued that although productivity gains are evident, the true value AI tools can unlock for businesses comes in the time developers can save, which can be reallocated on more activities that add more value to the organization, such as better collaboration.
“The real value from GenAI tooling hasn't just been the pure productivity benefits, but what we do with the extra time saved in the development process. As a firm, we have been encouraging staff to see GenAI as a tool in their toolbox that enables them to spend more time on ‘value-add’ activities, such as collaboration, talking to customers, and coaching junior team members,” he stated.
“If a firm deploys AI technology without an understanding and guidance on what teams should do with any extra time they save in the development process, they will be missing out on an enormous opportunity for GenAI to be the enabler of innovation and collaboration.”
Hesitant firms’ staff more likely to distrust AI tools
One key finding from GitHub’s research indicated that the way an organization promotes using AI coding tools can have a significant impact on staff perception.
For instance, 48% of those working in companies that actively promoted the use of AI tools said their toolchains were ‘simple to use’, compared to 65% of those working in organizations with a neutral stance on AI, who described their toolchains as ‘complex’.
Allan noted that CreateFuture also found there was a link between an organization’s stance on AI and how developers viewed the tools.
“The correlation between organizational AI stance and developer experience is noteworthy. In our experiments, we found that productivity gains increased over time as we enhanced training, built specific GenAI workflows, and saw broader technology adoption.”
Solomon Klappholz is a Staff Writer at ITPro. He has experience writing about the technologies that facilitate industrial manufacturing which led to him developing a particular interest in IT regulation, industrial infrastructure applications, and machine learning.
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