Nowadays, AI is everywhere we look. In the sea of information (and misinformation) it can be difficult to pinpoint exactly how it is used in the IT sector – specifically, Software Development. To cut through the noise, we spoke with Roberto, one of our Senior Software Engineers at MWS to get his views on how AI is shaping our industry. Want some clarity? Read on.
How does AI help, really?
AI, is becoming a big help for people who build software. It’s changing how developers work every day, making their jobs easier and faster. With smart tools, AI helps write code, find and fix problems, and even improve the quality of the software. In the questions below, we look at how AI supports developers with their daily tasks and helps turn ideas into working software more quickly.
How is AI currently assisting software developers in their daily tasks?
AI is currently acting as a productivity partner. Tools like GitHub, CoPilot, Amazon CodeWhisperer, and ChatGPT help developers write code faster, automate repetitive tasks (like writing boilerplate), generate unit tests, explain complex code, and even refactor legacy systems. AI also supports troubleshooting by suggesting fixes or debugging hints in real time.
What are some practical ways AI is improving software quality or speeding up development cycles?
AI enhances code quality through automated code reviews, intelligent static analysis, and test generation. It can help us catch bugs earlier in the cycle, prioritise issues, and recommend improvements based on large-scale learned patterns. For speed, AI helps with faster prototyping, infrastructure-as-code, and automating documentation – shrinking the time from the initial idea to deployment.
What are the biggest challenges or concerns when using AI in Software Development?
As always, AI does bring some concerns:
Code Correctness: AI suggestions aren’t always accurate or optimal
Security & Privacy: There’s risk of AI inadvertently introducing vulnerabilities or leaking sensitive data
Bias & IP issues: Generated code may unknowingly replicate copyrighted material or reflect biases in training data
Over-reliance: Developers may become dependent on AI without understanding the underlying logic
Can you share an example of a project where AI made a meaningful difference in the development process?
Using AI, we were able to refactor really old PHP code to up-to-date React within a few hours when it was estimated to take a couple of months. Now, it wasn’t perfect, but it worked!
How do AI-powered tools fit into modern development workflows or CI/CD pipelines?
AI tools are becoming embedded throughout the pipeline – from smart linting and test generation in pre-commit hooks to AI-assisted deployment strategies and anomaly detection in production. In CI/CD, AI helps predict build failures, prioritise test execution, and optimise resource allocation, making the entire pipeline more adaptive and efficient.
What skills or knowledge areas should engineers focus on to work effectively alongside AI technologies?
Of course, as things evolve, it is important to keep up to date. Here are a few tips for engineers wanting to work with AI:
Prompt engineering: Learning how to phrase effective queries to AI tools.
Understanding AI limitations: Knowing when to trust and when to verify AI outputs.
Data handling: Awareness of data privacy, governance, and bias issues.
Model basics: A foundational grasp of how LLMs and ML models work helps contextualise their output.
Version control + automation: Integrating AI into automated workflows via scripting or API usage.
What do you see as the most exciting opportunities AI brings to the future of software and digital solutions?
A number of things excite me about AI, but I’ll stick to some key ones:
Autonomous agents that can manage tasks end-to-end.
Conversational UIs that make complex systems more user-friendly.
Hyper-personalised apps that adapt to user behaviour in real time.
Automatic documentation, migration, and compliance for legacy systems.
Roberto Tsvetanov
Ultimately, AI will allow developers to focus more on creative architecture and problem-solving, while machines handle the repetitive, low-level tasks.
At MWS, we are committed to staying ahead of the curve, and that includes researching deeply into how AI can create more efficient, secure and optimised practices – so that our clients don’t have to. Stay tuned for more insights.
