Engineering in the age of AI is radically different

It’s like having an invisible friendPrathibha T C, team lead, India Operation Centre, ABB GISPL

During my student days, when I was overwhelmed with thousands of lines of code or debugging for hours, I frequently wished for an invisible friend who could write code for me and correct errors instantly without straining my eyes and back.

That dream has now become a reality with GitHub Copilot. For the last nine years, I have focused on C# .NET development. AI has genuinely brought my childhood dream to life; it operates beyond my expectations. Using GitHub Copilot in Visual Studio feels like magic. It understands my instructions, generates hundreds of lines of code within seconds, and helps resolve both compile-time and runtime errors, allowing me to get more creative with my work.

Traditional coding skills, such as manually writing and debugging thousands of lines of code from scratch, are becoming less central. AI can assist us in creating design specification documents and user manuals, becoming an indispensable part of my daily development workflow.Explore vast solution spaces in secondsDivya C Nambiar, enior cloud expert, Data Analytics, HPE India

The most tangible way AI has transformed my work is by becoming an on-demand thought partner, completely redefining how I design and build.

The biggest shift I’ve noticed is in the skills I lean on. I’ve moved away from memorising every detail of code syntax towards more strategic thinking. Older skills, like remembering obscure syntax, are less central now. Instead, the ability to validate and refine AI-generated code has become crucial for quality and reliability.

My most eye-opening experience has been watching AI generate creative solutions that I might not have considered myself.

For instance, using tools like Optuna for hyperparameter tuning in machine learning or LucidCharts AI for architectural diagrams has shown me how AI can explore vast solution spaces in seconds. It’s like having a team of brilliant minds to brainstorm with, pushing the boundaries of what’s possible and helping me deliver more innovative and robust solutions.Build faster and more accuratelyMadhuri D Pillai, Programme Director, Data and AI, IBM India Software Labs

In my current role, I have seen how AI is reshaping how software is designed, built, and maintained.

Today, instead of spending time on low-level coding tasks like boilerplate writing and syntax, I now focus more on framing clear prompts, validating AI outputs, and effectively engaging in pair programming with AI. AI tools and techniques also enhance productivity. Testing automation is a great example. Earlier, I would spend hours defining inputs, outputs, and edge scenarios.

Now, AI generates a baseline suite almost instantly and suggests positive, negative, and boundary cases that I refine with business logic.

AI can also analyse project requirements, design documentation, and user behaviours to align tests with realworld scenarios, allowing my team to focus on complex cases and innovation. With emerging self-healing capabilities, test cases can evolve continuously, reducing maintenance overhead and automating tasks from end to end. The result – I see faster cycles, stronger coverage, higher accuracy, and greater team productivity and engagement.You can reverse engineer legacy code with easeSharath Kashyap, Senior Director, Software Engineering, Epsilon India

If you’re still debating AI adoption, you’re already falling behind — this is the next industrial revolution. Much like past technology revolutions, AI is redefining the future of engineering and embracing it is no longer optional if you want to lead rather than follow. One of the most eye-opening experiences I’ve had in AI comes from its combined impact on creativity and business processes.

At Epsilon’s AI CoE, we’re witnessing how AI’s ability to generate visuals, videos, and creative content is revolutionising how ideas are expressed and products are designed.

On the technical side, generative AI and large language models have amazed me with their power to reverse-engineer legacy code, effortlessly produce detailed documentation, and convert workshop call transcripts into comprehensive Business Requirement Documents.

We’re also automating test case generation, load testing, and producing contextual code by understanding product documentation’s complexities — all through intuitive prompts. This fusion of creative and technical enhancement is transforming what engineers can achieve and pushing innovation at unprecedented speeds.I’ve seen AI’s impact on cancer careDebasmita Ghosh, ssociate director, center for advanced AI, advanced technology centers global network, Accenture

I’ve witnessed the transformational impact of AI in a project aimed at enhancing personalised treatment options for cancer patients.

By leveraging AI and image analytics to analyse cell distribution and correlate it with genomic data, we’ve gained valuable insights into diverse patient responses to similar treatments. To me, this project exemplifies the powerful fusion of human ingenuity and AI capabilities to drive meaningful innovation.

With Gen AI agents contributing to tasks or portions of tasks such as design, coding, and documentation, I spend more time on strategic work such as devising innovative solution approaches, growing the practice, and upskilling my team.

The human-inthe-loop is indispensable for guiding and validating AI outcomes, contextualising them to the business challenge, and ensuring highquality outcomes. To stay ahead, I advise young professionals to develop foundational skills like machine learning, programming skills like Python, and expertise in cloud AI services, while cultivating critical thinking. Following tech blogs and podcasts, GitHub projects, and experimenting with new model releases are great ways to stay updated with the latest in applied AI research.Spilt problems into parts for AI to digestGourav Dhelaria, Engineering Lead, Whatfix

AI is fundamentally reshaping the engineering skillset. Older skills like memorising syntax or manually searching for solutions across the web are becoming less central. The new critical abilities are problem decomposition—breaking a task into smart, strategic prompts—and the skills to critically evaluate the AI’s output for logical errors or inefficiencies.

This has transformed my daily work. I now use AI as a pair programmer for everything from evaluating different technologies, writing unit tests, identifying edge case scenarios, to refactoring code.

AI helps build smarter systems. We were able to move our PII detection mechanism from fragile, rule-based systems to highly effective AI models. The most eyeopening moment was seeing AI in action on a quick PoC for one of my ideas.

The AI provided the entire roadmap and prototype code almost instantly, acting like an expert engineering consultant. It’s clear that the role of engineers is evolving from just writing code to architecting and directing AI-driven solutions. I’m excited for the times ahead.From manual grind to instant test scenariosVinodhini Baskaran, Manager, LatentView Analytics

One clear example where AI saves time is writing test cases from requirements.

Earlier, I would manually read through feature requirements or user stories, identify edge cases, and write out each test case line by line. This was time-consuming and often prone to human error or missed scenarios. Now, I can paste the user story or requirement into an AI tool, and it generates a comprehensive list of test scenarios, including edge cases like invalid inputs, timeout handling, or multi-device logins.

All I need to do is review, validate, and refine — saving nearly 60–70% of the time compared to manual creation. My most surprising experience using AI at work was realising just how much it can accelerate creativity and problemsolving—not just automating repetitive tasks. For example, early on, I used AI tools mainly to speed up routine coding or generate test cases. But what really opened my eyes was when I started leveraging AI as a collaborative partner during brainstorming sessions and solution design.AI acts like a knowledge connectorHimanshu Nayal, Director of IT IS & CISO, Hughes

AI has completely transformed our workflow, especially in our CI/CD pipelines. The old way was a time-consuming, reactive cycle: a build would fail from a simple error, and a developer would have to manually find and fix it. Today, our AI-powered pipeline is different. It acts as a smart assistant, catching and auto-resolving many of these common issues before the build is complete.We’re also using AI in our cybersecurity team to drastically reduce false positives in our security alerts. This means our team can spend their time on genuine threats, making our work more efficient and accurate than ever before. What has surprised me most is how AI helps us learn things outside our usual IT responsibilities. It acts like a knowledge connector. As an IT engineer, my world was once limited to technical issues.

Today, AI allows me to link my work to other departments, giving me a much broader view of the business. It’s an eye-opening shift.Creating test cases pre-AI was tediousSusan Mallick, Project Manager in Generative AI Centre of Competence, Siemens Healthineers Development Centre

Use of AI tools and solutions has helped to save time but it varies depending on the context. One of the biggest time-savers for me has been managing requirements and test cases.

Earlier, the process of creating user stories, acceptance criteria, and test cases was slow and tedious. It involved multiple discussions with stakeholders to understand highlevel requirements, breaking them into features, and testers going back and forth to clarify details.

The entire process was simplified with the help of an AIassisted requirement and test management tool. It generates user stories with proper acceptance criteria from high-level requirements.

Once the user is satisfied with the generated output, with a single click, the work item is created in the repository. This is helping requirement engineers, developers, and testers get faster access to complete the development and testing activities.It helped me understand serverless orchestrationVishwasa Navada K, Principal Solutions Architect, AntStack

Claude Code has transformed how I build serverless solutions.

When developing proof of concepts or adding features to existing codebases, it creates intelligent feedback loops that identify and fix issues before I even run the code. Previously, integrating new platforms into our tech stack meant hours of documentation reading and debugging. Now, Claude Code understands architectural patterns and suggests fixes in real time.

My most eye-opening experience has been discovering how AI accelerates learning itself – not just coding, but understanding new technological concepts and architectural patterns.

When constantly evaluating new serverless technologies, I engage in real-time conversations with Claude and ChatGPT that help me connect dots I might miss. It’s like having a senior engineer available 24/7 who explains concepts from multiple angles. The most surprising moment was when Claude Code helped me understand complex serverless orchestration by walking through underlying architectural principles – not just solving my immediate problem, but expanding my mental model of how these systems work together.

This changed how I approach learning.AI speeds up tasks I never expectedNeha Kulkarni, Software Developer, Advanced Analytics R&D, SAS R&D India

What has surprised me the most about using AI is how it supports so many parts of my daily work. Whether I am designing APIs, generating mock data, writing unit tests, or brainstorming UI layouts, AI speeds up tasks in ways I never expected. Sometimes it helps me catch tricky bugs.

Other times, it suggests cleaner code or points out edge cases I might have missed. It has even helped me draft documentation, write clearer emails, or take accurate meeting minutes, saving me time and mental energy.

AI has not replaced my role. If anything, it has made me faster, sharper, and more focused on solving the big-picture problems. It feels like having a super-helpful pair programmer who never gets tired or judges my weird 3 a.m.

questions. Honestly, the biggest lesson I have learnt is not to be afraid of AI. It is a powerful tool and by embracing it, I have more time and energy to focus on the creative and challenging parts of building great products.Pair programming is virtual nowPearlin Christabel, Senior Architect, Cognizant

My journey in IT began with pair programming where I huddled over a single monitor with my buddy. We learned, debugged, and grew together.

Coffee breaks were a crucial part of the process. Today, my co-pilot is an AI. It lives in my workspace, a tireless partner that generates code with a well-crafted prompt. While it can’t join me for coffee, its impact is just as profound. As a Senior Architect, my days were filled with understanding complex systems and creating diagrams, often slowed by scheduled meetings across time zones to gather insights from each team.

Now, instead of reaching out to multiple teams for understanding, I simply ask a chatbot (powered by Retrieval Augmented Generation) for a diagram, and it delivers in seconds, trained on a mountain of client documentation. The most valuable skill I’ve gained isn’t a new programming language, but the art of collaboration with my virtual partner, making our teamwork more efficient and focused than ever before.