At the 2026 CTO Summit in Bengaluru, industry executives highlighted that AI coding tools are speeding up code production but shifting engineering priorities towards system design, thorough code review, and robust testing to ensure software reliability.
- AI accelerates coding but increases focus on design and review.
- Traditional productivity metrics like lines of code become less relevant.
- Testing investment grows, with automation reaching 97% at some firms.
What happened
During the inaugural CTO Summit 2026 in Bengaluru, Indian tech executives discussed the evolving landscape of software engineering shaped by AI tools. These tools generate code rapidly, but engineers are increasingly dedicating time to system design, collaborative brainstorming, and rigorous code review. This represents a shift from merely writing code to ensuring quality and feasibility.
Panelists from leading companies such as JioHotstar, LeadSquared, Cashfree Payments, and Razorpay shared how AI affects workflows and organizational strategies. They emphasized that embracing AI is not simply about writing more code faster but involves adapting processes to maintain software reliability and customer impact.
Why it matters
The discussion underlined that conventional engineering productivity metrics like pull requests or lines of code become insufficient or misleading in the AI era. Instead, metrics focusing on speed to production, product stability, availability, and real customer benefits are gaining prominence. This reflects a maturation of engineering priorities driven by AI capabilities.
Moreover, companies like Cashfree Payments have increased investments in virtual environments and automated testing frameworks, with a reported 97% automation of test cases. Such efforts highlight the growing importance of thorough validation as AI-generated code requires strong foundations in monitoring and performance detection to avoid downstream issues.
What to watch next
Organizations are rethinking how employees access and utilize AI tools. For example, Eightfold moved from fixed budgets for AI tool usage towards dynamic top-ups, aiming to democratize AI adoption across both technical and non-technical teams. This shift indicates AI’s expanding role beyond engineering into other business functions.
As Indian technology companies continue to integrate AI coding, the emphasis on holistic productivity, including design, review, testing, and delivery outcomes, will be crucial. Future tracking should focus on the development of new performance metrics and infrastructure investments that ensure AI-augmented engineering leads to tangible business value.