Simplicity can outperform complexity if it delivers better ROI

AI development demands iterative learning rather than a one-time, linear approach. Dr Hemant Misra warns of siloed data, lost knowledge, and overspending on complex models without clear ROI. Emphasizing robust data governance and mindful infrastructure usage, he showcases how digital-native practices guide sustainable AI growth.

- Praneeta

WITH MORE than 27 years of experience in the field of AI, Hemant Misra has been at the forefront of developing, deploying, and scaling data-driven solutions. Currently heading the Data Science team at Simpl, he focuses on translating AI models into real-world production systems ranging from fraud detection to complex chatbot solutions.

In an exclusive conversation with CIO&Leader, Hemant Misra discusses the biggest challenges enterprises face in scaling AI initiatives, how organizations can learn from digital-native companies, and the importance of data governance. Below is an edited version of the interview:

There is a mindset which needs to be changed that we may start with something which is small, then iteratively we learn and then we move to the system which is going to help us move the needle.

CIO&Leader: What are the biggest challenges enterprises are facing in terms of scaling AI, and what's needed for the industry to overcome them?

HEMANT MISRA: There are two major challenges. First, AI systems don’t follow the same linear development process as typical software systems. Traditional software often has fixed specifications before deployment, but AI initiatives need iterative learning and refinement. This requires a mindset shift: start with smaller projects, learn quickly, and improve iteratively, rather than expecting a fully production-ready system from day one.