Rebuild vs Refactor: A Spec-Driven Strategy for Growth & Modernization

In this episode of AI ThoughtMakers, Suresh Konakanchi shares a hard truth many teams discover too late: AI prototypes rarely fail because of the model — they fail because they were never designed for production.Today, AI can generate polished demos in days. But behind impressive interfaces and fast-moving prototypes, most products still lack the foundations required for real-world reliability, scalability, and long-term growth.This conversation explores the critical gap between prototype and production — and why many organizations get trapped in endless rebuild cycles instead of sustainable progress.Suresh breaks down what actually makes AI systems production-ready, including: Spec-driven development and why clarity matters before coding  The hidden risks behind “demo-ready” AI products  Production checklists teams often ignore  Scalability, observability, reliability, and edge-case handling  Why poorly defined requirements lead to repeated refactors  The importance of understanding AI limitations before deployment  Building systems that can evolve without constant rebuilding If you’re building AI products today, this episode challenges one important question:Are you building something that only looks production-ready — or something truly built to scale?Connect with the SpeakersSuresh Konakanchi on LinkedInPrem Goswami on LinkedInAbout AI ThoughtMakersAI ThoughtMakers is a podcast series exploring how AI is transforming products, engineering, business strategy, and decision-making through conversations with industry leaders and technology experts.