AI is reshaping how we code, debug, and collaborate. From Copilot to automation, it is changing software development in ways worth exploring.
Artificial Intelligence (AI) is everywhere—powering change in healthcare, education, energy, and beyond. But in software development, its role runs deeper, reshaping fundamentals and giving developers new partners in tools like GitHub Copilot.
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From Writing Code to Intelligent Collaboration
Traditionally, our work as developers revolved around writing code, debugging, and testing. But today, the picture has changed. We are not just writing code, but also collaborating intelligently with AI.
When I say collaboration, I mean that AI has shifted from being just another tool to becoming a true partner in our workflow. Developers now work alongside AI systems, co-creating solutions, refining code, and even predicting potential improvements. This collaboration represents a movement from the science of code to intelligent collaboration.
For example, tools like GitHub Copilot no longer simply suggest snippets, they often provide complete solutions, debug issues, or generate test cases. In many ways, AI has become a co-programmer rather than a passive assistant.
From Automation to Intelligent Automation
For years, the industry focused on automation, streamlining repetitive tasks and improving efficiency. But with AI in the mix, we are talking about intelligent automation.
Platforms like Blue Prism, for instance, began with robotic process automation. Now, they are evolving into intelligent automation, where processes are not just automated but are also adaptive, data-driven, and context-aware.
This is precisely what is happening in software development. Developers are no longer just code writers, they are co-creators with AI, embedding intelligence into products whenever it adds real value for end users and businesses.
But here is the critical point—not every software product needs AI. The main questions organisations must ask are:
- Does AI improve productivity for developers?
- Does AI deliver measurable value to end users?
- Does it contribute to revenue growth?
If the answer is “no,” then adding AI is just noise. Business leaders are rightly prioritising usefulness and ROI over hype.
Pain Points of Traditional Development and How AI Helps
While I hesitate to call it traditional development, there is a clear and significant shift in how work is done. Key pain points AI is addressing include:
1. Manual debugging
Debugging has always been time-consuming. Tools like GitHub Copilot now highlight errors in real time, suggesting fixes instantly.
2. Repetitive coding
Starting projects often meant rewriting boilerplate code. Now, a single line or keyword can trigger AI to suggest entire code blocks, saving significant time.
3. Testing
Unit testing used to be a burden. Today, AI generates test cases and testing frameworks, reducing repetitive effort.
4. Onboarding new developers
In large organisations, locating project files across platforms like SharePoint, GitHub, or ServiceNow can be overwhelming for new hires. AI-powered chatbots or plugins centralise knowledge, making onboarding faster and smoother.
5. Skill gaps
This is something I experienced firsthand. I once joined a project built in Golang, even though my core expertise was in Java. Initially, I had no idea where to start. GitHub Copilot became my guide, explaining the workflow, clarifying syntax, and even breaking down code line by line. It not only got the work done but also helped me learn Golang in the process.
GitHub Copilot: An AI Pair Programmer
Let’s talk specifically about GitHub Copilot, which I have personally used extensively.
We do not call it an AI programmer. Instead, it is an AI pair programmer—a collaborator that supports, but does not replace, human creativity. Here are some ways it has transformed my work:
- Fixing code in-line: Copilot highlights errors (with its familiar yellow star icon) and suggests corrections instantly
- Understanding new code. Perfect for working with unfamiliar languages or legacy projects
- Unit testing and documentation: Copilot auto-generates drafts, saving hours of manual effort
- PR summaries and repo interaction: It can generate pull request summaries and even “talk” to repositories
- Error handling: When errors appear in the terminal, Copilot immediately suggests possible fixes
One note of caution: Always ensure that sensitive code or data is not shared with these tools. Many organisations are rightly concerned about data security in AI-assisted workflows.
Final Thoughts: AI in Software Development is the Future of Coding
AI is no longer just an accessory in software development; it is becoming a collaborator. From debugging and testing to onboarding and documentation, tools like GitHub Copilot are easing pain points that developers have lived with for years. But while the benefits are clear, the central point is that AI should add genuine value. Whether it is improving developer productivity or enhancing the end-user experience, the goal must always be impact, not hype.
As developers, we should embrace AI as a thought partner, a co-pilot, and a catalyst for innovation, while also being mindful of its limitations and risks. The journey from ‘writing code’ to ‘intelligent collaboration’ has just begun, and I am excited to see where it is taking us.
The article is based on the talk titled ‘Transforming Software Development with AI’ given by Harsh Sharma (Shell) at AIDevCon 2025. It has been transcribed and developed by Vidushi Saxena, a journalist at EFY.
