Artificial Intelligence is evolving in ways that were unimaginable just a few years ago. Initially seen as a tool for automating tasks, AI is now reshaping how we create, solve problems, and innovate across industries. In product development, AI is no longer a back-end tool but a collaborator in the product developmental process – from accelerating design to streamlining decision-making.
As Murugesapandian, Chief Technology Officer at Siam Computing explains, AI is transforming product development in profound ways, offering unprecedented speed and efficiency, while challenging the limits of human imagination.
But this transformation is not without its caveats. Murugesh explains that while AI excels at tasks like data analysis and rapid prototyping, it still relies on human expertise to address complex reasoning and industry-specific nuances. The future of product development lies in this partnership – humans bringing creativity and intuition, and AI amplifying those capabilities to achieve what once seemed impossible, be it designing software, building hardware, or crafting new experiences.
In a conversation with Team ProdWrks, Murugesapandiam shares his insights on the evolving role of AI in product development, the challenges it addresses, and the exciting possibilities it unlocks. With his extensive experience in programming, design, and leadership, he paints a compelling picture of how AI is poised to redefine innovation and collaboration in years to come.
This interview has been edited for clarity and length
Q1. Hello Murugesh. Can you give us a brief overview of your professional journey and how it led you to your current role as the CTO of Siam Computing?
Murugesh: I’ve loved programming since I was in school, which is why I decided to study computer science in Madurai. I always knew I wanted to be a developer. I began my career in the gaming industry, working on UI design and 3D models, but eventually, I switched back to programming because that’s what resonated with me.
Over the years, I’ve learned different programming languages like C#, Java, VB.NET, Python, Node.js, Angular, and React. I’ve worked on desktop and mobile apps, game development, and areas like system administration, network administration, and DevOps.
This diverse experience has helped me understand how everything fits together from a technical perspective. As a CTO, I now use that knowledge to ensure everything runs smoothly. My curiosity and drive to learn have been the biggest factors in my career progression.
Q2. What excites you most about working in product development today?
Murugesh: What excites me most is solving real problems for customers. I enjoy creating simple solutions that directly address their needs. I also love taking an idea and turning it into something concrete, guiding it through the engineering process. That part of the job is deeply fulfilling.
What I’m good at is figuring out what’s technically possible and finding ways to make it work. My experience allows me to evaluate different solutions and select the one that best suits the project. The challenge of solving these problems and seeing them come to life is what drives me.
Q3. Can you describe a typical day in your role? Where does AI fit into your routine?
Murugesh: A typical day for me involves a mix of strategy, problem-solving, and hands-on work. I start by brainstorming with the product team, refining ideas, and planning our roadmap. Then, we dive into feasibility studies and R&D to explore what’s possible and how to approach it. I also deal with implementation challenges, ensuring efficiency in execution.
AI is part of everything I do–from planning and design to development. AI helps us gather inputs, define the product, organize information, and even generate data when needed. AI helps us streamline workflows and ensures everything moves forward smoothly.
Q4. What are the major challenges you face in your workflow?
Murugesh: My workflow involves balancing long-term goals with daily operational challenges, which can be difficult. For example, I might be deep into R&D work, but if there’s a production issue—like a site outage, an app crash, or a feature not working—it gets escalated to me if the product team can’t resolve it.
These issues pull me away from my current tasks, requiring a shift in focus. Once the problem is solved, it takes time to get back into the flow of my original work. This constant switching makes the role of the CTO challenging. Managing that balance and regaining my flow state is one of the biggest challenges I face as a CTO.
Q5. How does AI impact decision-making in your role?
Murugesh: AI significantly aids decision-making, but the final call is always mine. While AI can handle a lot of the preliminary work, I still need to verify its outputs. For example, if I ask it to gather information from specific websites, it can provide a summary. But for complex topics like blockchain, it offers high-level information rather than deep insights.
That’s where I double-check, verify, and ensure the accuracy of the information.
Verification often involves manual research, cross-checking documents, or relying on my experience. If I’m familiar with the topic, I can quickly validate what’s right and move forward. If I’m not sure, I dive deeper to ensure accuracy. While AI accelerates decision-making by providing data and options, the responsibility for accuracy ultimately rests with me.
Q6. What are the biggest challenges currently faced in product development?
Murugesh: One major challenge is gathering insights from domain experts. For example, in banking, it’s important to understand daily operations and what needs automation. However, accessing this information is tough because bankers are often too busy to explain their workflows, which makes it harder to design and build effective solutions.
Another challenge is understanding and adapting to different industries. If you’re familiar with healthcare, but not others like retail or banking, the knowledge gap can cause friction during brainstorming. Differences in terminology and processes can lead to confusion.
From a technical perspective, creating something unique is difficult. If a product resembles another in look or functionality, it won’t stand out. Engineers often have to build custom solutions, as existing libraries and frameworks don’t fully support new ideas.
Lastly, maintaining a product after it’s built is crucial. Regular updates and monitoring are essential to avoid issues with browsers, mobile devices, or security. Without this upkeep, the product risks failing over time and becoming obsolete.
Q7. Are there tasks or processes you wish AI could handle more effectively?
Murugesh: AI still struggles with decision-making and reasoning. Right now, it can provide suggestions, options, and summaries, but its reasoning isn’t as reliable as a human’s. The main problem is that AI doesn’t give definite answers; It works on probabilities. So, its responses might be right or wrong, and you can’t always rely on them.
For AI to improve, it needs to provide more consistent and reliable answers. If it becomes more deterministic—giving clear and dependable outputs—it would be much more useful. Reliability is key, and until AI gets better at this, it won’t fully meet expectations.
Q8. Can you share an example where AI helped you overcome a particularly tough obstacle?
Murugesh: I wouldn’t say I’ve faced particularly tough obstacles, but AI has helped reduce my workload and save time. Since I’m involved in many tasks, I don’t always have the luxury to focus on one for long. In one instance, AI helped me create a proof of concept (POC) in just one day—a task that used to take three to four days.
One of the most challenging parts of my work is data preprocessing. For example, when dealing with thousands of entries in a CSV or Excel file, you need to process the data, understand it, clean it, and prepare it into a meaningful format. This requires both domain knowledge and math skills.
Using AI-based solutions made this process much easier. AI provided suggestions and options for handling the data I gave it, speeding up the preparation phase. Preprocessing large amounts of data is always one of the toughest challenges, but AI has made it far more manageable.
Q9. How do you envision AI evolving in your field over the next 5 years?
Murugesh: Over the next five years, I see AI evolving in two key ways: better reasoning and deterministic decision-making. Right now, AI makes predictions based on probabilities but doesn’t truly “reason” or provide fully reliable answers. Once AI masters this, it’ll take over repetitive tasks, free up our time, and create new opportunities in the AI space.
Right now, we’re in a phase of “agentic development,” where AI handles specific tasks. Just like how traditional development evolved from server-based systems to SaaS, AI is now stepping in. In the future, AI might even write application code on its own.
A fascinating concept is “multi-agent systems,” where different AI models take on specialized roles. For instance, one AI gathers information, another writes, and a third reviews the work. These agents could collaborate seamlessly, like a virtual team. Right now, these systems are basic, but they’ll likely become more advanced, capable of handling complex workflows and improving reasoning as they specialize further.
Interactions with AI will also become more natural, moving from text-based to voice-based commands. Instead of centralized systems like Alexa, devices themselves might have built-in AI. For example, you could tell an elevator to go to the third floor or talk directly to appliances like fans or fridges. AI will integrate more deeply into our daily lives.
This evolution won’t stop at software—it’s already transforming hardware. In robotics and transportation, we’re seeing exciting innovations, like driverless cars and bikes that you can book on Uber. These technologies will expand beyond big cities, making them accessible to everyone. AI is set to reshape not just how we work, but how we live.
Q10. What role do you think human creativity will play as AI takes on more responsibilities?
Murugesh: In the past, when people wanted to create art, they didn’t have fancy brushes or paints. They used stones to draw and somehow even managed to create color paintings. Over time, brushes came along, and painting evolved. Now, we have AI, which can be seen as a new kind of tool or “brush.”
I believe that human creativity will keep growing and becoming more advanced. With AI, we can create things in ways we couldn’t before. It’s not about AI replacing human creativity; it’s about AI becoming a tool that supports and enhances that creativity. The future of art and creation will be exciting, with AI helping to bring ideas to life, no matter what field you’re in.
Q11. Are there any futuristic AI capabilities that excite or inspire you the most?
Murugesh: One of the most exciting AI developments to me is the new application Stanford recently launched. It’s still in trial, but it’s already impressive. You give it a topic, and it can analyze hundreds or even thousands of articles, then quickly generate a basic outline and summary, pointing out what needs to be changed to improve the system. The speed and efficiency of this are mind-blowing. What used to take months of manual work, like reading through thousands of paragraphs for a journal contribution, can now be done in just 20-30 minutes.
Another exciting development is AI tools that can help with coding. If you know what needs to be done, you can provide inputs, and the AI can generate a working prototype in just a couple of hours. Tools like Bolt or VoCursor AI are already pretty advanced. They’re not perfect yet, but they’re already at 60-80% of what you need. What used to take weeks to develop can now be done in just a couple of days. To me, that’s incredibly exciting and shows just how much potential AI has to speed up and improve our work.
Q12. In your opinion, what one skill will product developers need most to thrive alongside AI in the coming years?
Murugesh: Learning and adaptation are the most important skills to have. The tools and methods may change, but the ability to learn must stay constant. If someone isn’t willing to learn how to use tools like AI, they’ll struggle to keep up with the new era.
Right now, we’re in the AI era. In the future, it could be AGI or something completely different. But what will keep you moving forward is your ability to learn. Learning drives growth, keeps you motivated, and helps you take the next steps. Once you commit to learning, everything else—understanding, adapting, and progressing—falls into place naturally.
This applies to everyone, not just developers or technical people. Learning and adapting are the foundation for staying relevant and thriving, no matter what the future brings.
