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 Speakers

• Suresh Konakanchi on LinkedIn
• Prem Goswami on LinkedIn

About AI ThoughtMakers

AI 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.