"How To Enhance Software Developers’ Productivity With GenAI" article in Techstrong.AI, June 2, 2025, ghostwritten by Don Talend

1. How To Enhance Software
Developers’ Productivity With
GenAI
By Vivek Kumar Agrawal
June 2, 2025
Software development is one profession that can achieve a significant productivity boost from the use of
generative artificial intelligence (GenAI). GenAI provides measurable gains in developer productivity by
performing numerous repetitive tasks, freeing developers to fulfill duties better suited to humans, such
as coding.
A company can maximize GenAI benefits by incorporating it into development workflows gradually and
cost-effectively, continuously analyzing its performance, and adjusting the human/GenAI division of
labor as needed. A boost in developer productivity often results in a substantial improvement in
company profitability.

2. More Time Spent on Non-Coding Tasks​
A typical software developer spends a disproportionate amount of time on non-coding tasks, which
reduces their productivity. A study of Microsoft developers found that they devote more time to
communication and meetings than coding (about 12% versus 11%) and only 9% to debugging code. On
the other hand, 20% of these developers wanted to spend more time coding. According to Amazon Web
Services, the company’s developers spend just one hour daily on actual coding and the remainder of the
time on tasks such as fixing problems and hunting down vulnerabilities.
Software quality and profitability data indicate that performing these non-coding tasks is not suited for
human developers. In 2022, the Consortium for Information & Software Quality estimated that the cost
of poor software quality in the United States was at least $2.41 trillion, although cybercrime also plays a
significant role. Additionally, the accumulated software technical debt (TD) was an estimated $1.52
trillion. As technologies evolve, these figures will continue to grow.
A Dramatic and Measurable Productivity Impact​
Time is money. A Google Cloud State of Development Operations study revealed that, compared with
low-performing developers, elite developers deliver 127 times shorter lead times, 182 times more
deployments per year, an eight times lower change failure rate, and 2,293 times shorter failed
deployment recovery times. GenAI tools, like internal chatbots, can improve developers’ productivity by
automating repetitive tasks such as:
●​ Code generation and debugging support. Tools such as GitHub Copilot, Windsurf Editor and
Supermaven can autocomplete code, generate entire functions and suggest relevant snippets,
saving developers keystrokes and enabling them to code faster. Also, these tools guide fixes by
analyzing error logs and accessing historical records of similar bug fixes.
●​ Code review assistance. Tools like GitHub Copilot and Qodo help to speed up code reviews with
intelligent suggestions and automated insights.
●​ Automated testing. These tools help developers write test cases and check code quality by
ensuring that developers follow the company’s established code-writing practices.
●​ Internal knowledge base access. GenAI chatbots can be trained on a company’s large language
model (LLM) to search its proprietary internal documentation of historical coding. These tools
provide the foundational coding structure for a current project, drastically reducing the amount
of required coding “from scratch.”
According to a study by Microsoft, MIT and other university researchers, the impact of GenAI chatbots
on developer productivity can be dramatic and measurable. The researchers found that access to GitHub
Copilot increased output — the number of completed weekly tasks — by 26% overall, 27-39% among
recent hires and junior developers, and 8-13% among senior developers.

3. How to Implement GenAI Profitably
The application of six best practices improves the odds of profitable GenAI implementation.
1. Trust but verify the data. GenAI chatbots well-trained on a company’s LLM will generally generate
accurate foundational code. Still, meticulous human validation provides the necessary quality control.
1.​ Ensure proprietary data security. Establishing data security protocols and monitoring adherence
to the protocols can ensure that the proprietary internal documentation used in GenAI models
remains proprietary.
2.​ Optimize LLM build costs. Building an internal LLM can be a significant investment. Staying
focused on the cost can optimize the investment.
3.​ Implement gradually and adjust as needed. “Crawl” is the appropriate stage in a crawl-walk-run
implementation process for gathering feedback from developers on the impact of GenAI on
workflows. This valuable feedback can inform adjustments before expanding the use of GenAI in
development workflows.
4.​ Make GenAI an optional, rather than mandatory, gatekeeper. Tools such as the Sonar plug-in
offer developers the flexibility to use GenAI as an optional quality control gatekeeper. Developers
can improve their static code analyses by adding a step in code review or test case writing that
incorporates these tools without unduly disrupting their workflows.
5.​ Analyze developers’ time and task allocations. After GenAI tools are incorporated into
development workflows, it’s a good idea to evaluate developers’ task allocations. Time allocation
key performance indicators (KPIs) include coding, deploying code, debugging and other
repetitive tasks. If GenAI is used effectively, coding time should exceed the 11% allocation cited
earlier. Eventually, setting realistic GenAI-aided developer productivity KPIs will follow suit.
Potential for More and Better Development Jobs
After benchmarking appropriate time allocations for human developers based on using GenAI in their
workflows, organizations can then implement GenAI and focus on continuously improving developer
productivity and work quality. With this approach, organizations change how developers do their jobs.
At the same time, this evolution can result in higher worker and customer satisfaction, and a worthwhile
return on investment. Just as the introduction of the personal computer created many new jobs rather
than eliminating them, GenAI has tremendous potential to yield a net gain in business-critical jobs.

4. About the Author
Vivek Agrawal is a senior director of software engineering for a leading e-commerce
retailer. He has over 17 years of experience working with large-scale systems,
information retrieval, runtime scaling, and performance. He currently manages a
team of over 80 engineers responsible for a search runtime platform serving more
than 200 million requests per day. Vivek holds an M.tech in electrical engineering
from IIT Kanpur in Kanpur, India
https://techstrong.ai/aiops/how-to-enhance-software-developers-productivity-with-genai/
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