Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I’ve been in IT for over 23 years now. Most of my work has been around AI and digital transformation, helping companies figure out where AI can make a difference. I’ve worked across manufacturing, retail, utilities, and a few other industries. My focus is always on taking AI from being just an idea to something that shows business results. Currently, I head the AI practice at Tech Mahindra, and I manage the overall development of artificial intelligence technologies, establishing enterprise standards and strategies for AI adoption, developing policies that support the responsible and ethical use of AI technology.

Q2. How are you seeing AI adoption vary across key industries like BFSI, telecom, manufacturing, and healthcare?

It’s moving at a different pace in each sector. BFSI is way ahead with fraud detection, credit risk checks, and chatbots. Telecom is using AI for network monitoring and automating customer service. Manufacturing is picking up with predictive maintenance and quality inspections. Healthcare is very promising, but they are careful because of compliance, so we see more work in diagnostics and patient engagement.

Q3. Which AI subfields—generative AI, computer vision, NLP, or reinforcement learning—are gaining the most traction right now?

Generative AI and NLP are leading the race because of all the chatbots, document summarisation, and content creation use cases. Computer vision is powerful in industries where visual checks matter—like manufacturing or security. Reinforcement learning is still a bit niche, primarily in R&D or advanced automation.

Q4. In enterprise AI engagements, what share tends to come from cloud-based deployments compared to on-premise or edge?

I’d say roughly 60–70% is cloud now because it’s easier to start and scale. On-premise is still there for cases where the data is sensitive or regulations demand it. Edge AI is growing fast in areas where you need instant decisions, like manufacturing lines or retail stores.

Q5. How do clients balance the importance of proven AI models versus the flexibility to build custom solutions?

Usually, they start with something proven so they can see results quickly. If the problem is particular or the data is unique, they go for custom models. Many times, it’s a mix—they take a pre-trained model and fine-tune it for their needs.

Q6. Which companies do you think are setting the benchmark for AI services in terms of innovation and client impact?

The big cloud players like Microsoft, Google, and AWS are driving significant innovation. On the services side, companies like Accenture, Infosys, and TCS are doing well in scaling AI. And many startups are doing great work in niche AI areas, too.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

I’d ask—how are you making sure your AI projects are delivering business outcomes, not just looking good in a demo? That one question tells you how serious they are about real impact.