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Is your team using generative AI to enhance code quality, expedite delivery, and reduce time spent per sprint? Or are you still in the experimentation and exploration phase? Wherever you are on this journey, you can’t deny the fact that Gen AI is increasingly changing our reality today. It’s becoming remarkably effective at writing code and performing related tasks like testing and QA. Tools like GitHub Copilot, ChatGPT, and Tabnine help programmers by automating tedious tasks and streamlining their work.
And this doesn’t appear like fleeting hype. According to a Market Research Future report, the generative AI in software development lifecycle (SDLC) market is expected to expand from $0.25 billion in 2025 to $75.3 billion by 2035.
Before generative AI, an engineer had to extract requirements from lengthy technical documents and meetings manually. Prepare UI/UX mockups from scratch. Write and debug code manually. Reactive troubleshooting and log analysis.
But the entry of Gen AI has flipped this script. Productivity has skyrocketed. Repetitive, manual work has been reduced. But beneath this, the real question remains: How did AI revolutionize the SDLC? In this article, we explore that and more.
Where Gen AI Can Be Effective
LLMs are proving to be wonderful 24/7 assistants in SDLC. It automates repetitive, time-consuming tasks. Frees engineers to focus on architecture, business logic, and innovation. Let’s take a closer look at how Gen AI is adding value to SDLC:
Possibilities with Gen AI in software development are both desirable and overwhelming. It can help increase productivity and speed up timelines.
The Other Side of the Coin
While the advantages are hard to miss, it raises two questions.
First, about how safe is our information? Can we use confidential client information to fetch output faster? Isn’t it risky? What are the chances that these ChatGPT chats are private? Recent investigations reveal that Meta AI’s app marks private chats as public, raising privacy concerns. This has to be analyzed.
Second, and the most important one, what would be the future role of developers in the era of automation? The advent of AI has impacted multiple service sector profiles. From writing to designers, digital marketing, data entry, and many more. And some reports do outline a future different from how we might have imagined it five years ago. Researchers at the U.S. Department of Energy’s Oak Ridge National Laboratory mention that machines, rather than humans, will write most of their code by 2040.
However, whether this will be the case is not within the scope of our discussion today. For now, much like the other profiles, programmers will be needed. But the nature of their work and the required skills will change somewhat. And for that, we take you through the Gen AI hype check.
Where the Hype Meets Reality
- The generated output is sound but not revolutionary (at least, not yet): With the help of Gen AI, developers report faster iteration, especially when writing boilerplate or standard patterns. It might work for a well-defined problem or when the context is clear. However, for innovative, domain-specific logic and performance-critical code, human oversight remains non-negotiable. You can’t rely on Generative AI/LLM tools for such projects. For example, let’s consider legacy modernization. Systems like IBM AS400 and COBOL have powered enterprises for so many years. But with time, their effectiveness has reduced as they’re not aligned with today’s digitally empowered user base. To maintain them or improve their functions, you will need software developers who not only know how to work around those systems but are also updated with the new technologies.
An organization can’t risk losing that data. Depending on Gen AI tools to build advanced applications that integrate seamlessly with these heritage systems will be too much to ask. This is where the expertise of programmers remains paramount. Read how you can modernize legacy systems without disruption with AI agents. This is just one of the critical use cases. There are many more things. So, yes LLMs can accelerate the SDLC, but not replace the vital cog, i.e., humans.
- Test automation is quietly winning, but not without human oversight: LLMs excel at generating a variety of test cases, spotting gaps, and fixing errors. But that doesn’t mean we can keep human programmers out of the picture. Gen AI can’t decide what to test or interpret failures. Because people are unpredictable, for instance, an e-commerce order can be delayed for multiple reasons. And a customer who has ordered crucial supplies before leaving for the Mount Everest base camp trek may expect the order to arrive before they leave. But if the chatbot is not trained on contextual factors like urgency, delivery dependencies, or exceptions in user intent, it may fail to provide an empathetic or accurate response. A gen AI testing tool may not be able to test such variations. This is where human reasoning, years of professional expertise, and intuition stand tall.
- Documentation has never been easier; yet there is a catch: Gen AI can auto-generate docs, summarize meeting notes, and do so much more with a single prompt. It can reduce the time spent on manual, repetitive tasks, and provide consistency across large-scale projects. However, it can’t make decisions for you. It lacks contextual judgment and emotional maturity. For example, understanding why a particular logic was written or how certain choices can impact future scalability. That’s why how to interpret complex behavior still comes from programmers. They have worked on this for years, building awareness and intuition that’s hard for machines to replicate.
- AI still struggles with real-world complexity: Contextual limitations. Concerns around trust, over-reliance, and consistency. And integration friction persists. That’s why CTOs, CIOs, and even programmers are skeptical about using AI on proprietary code without guardrails. Humans are essential for providing context, validating outputs, and keeping AI in check. Because AI learns from historical patterns and data. And sometimes that data might reflect the world’s imperfections. Lastly, the AI solution needs to be ethical, responsible, and secure to use.
Final Thoughts
A recent survey of over 4,000 developers found that 76% of respondents admitted refactoring at least half of AI-generated code before it could be used. This shows that while technology improves convenience and comfort, it can’t be dependent upon entirely. Like other technologies, Gen AI also has its limitations. However, dismissing it as mere hype wouldn’t be entirely accurate. Because we have gone through how incredibly useful device it is. It can streamline requirement gathering and planning, write code faster, test multiple cases in seconds, and also proactively identify anomalies in real-time. Therefore, the key is to adopt LLMs strategically. Use it to reduce the toil without increasing risk. Most importantly, treat it as an assistant, a “strategic co-pilot”. Not a replacement for human expertise.
Because in the end, businesses are created by humans for humans. And Gen AI can help you increase efficiency like never before, but relying on them solely for great output may not fetch positive results in the long run. What are your thoughts?