About The Webinar
Everyone can build an AI agent today. The real problem is what happens after the demo.
In this session, Anju Chaudhary and Devanathan Desikan get into the actual reasons agentic AI projects stall between proof-of-concept and production and what it takes to close that gap. From the “architecture of isolation” that leaves enterprises with siloed pilots that can’t talk to each other, to the expensive refactoring tax that hits at day 91, this conversation draws from real delivery experience on both sides, platform and services. The discussion covers data readiness, governance, organizational change management, and why the first 30 to 60 days of an AI program can either set you up for scale or quietly guarantee failure.
TL;DR Agents work in demos. They break in production. This session is a frank, experience-driven conversation about why and what it actually takes to fix it.
Key Takeaways
- The pilot trap is an organizational problem, not a technical one. Governance gaps, unclear ownership, and siloed teams are the top reasons agents don’t make it to production. Most enterprises discover this too late, after the pilots are already built and the momentum has stalled.
- Day 91 is when the real cost shows up. The first 90 days are full of excitement. Different teams build cool pilots in isolation, everyone wants to show something. But by day 91, those pilots can’t talk to each other, don’t share data, and don’t follow the same guardrails. The refactoring tax that follows is far more expensive than building it right the first time.
- Your data has to be AI-ready before your agents can be enterprise-ready. If AI can’t access and understand your data in real time without someone manually curating it, autonomous operations are not possible. Data quality, a single source of truth, and a solid knowledge architecture are not nice-to-haves. They are the foundation.
- Enterprises don’t need another framework. They need a control plane. Most large enterprises already have multiple teams building on multiple frameworks. The answer is not to ask them to migrate or rebuild. It is to put a unified layer above all of it that gives you governance, observability, and audit trails across every agent regardless of where it was built.
- The teams winning are the ones showing working demos before the RFP conversation even starts. When you walk into a client with a live demo built on their data, the conversation shifts from “can you do this” to “how soon can we start.” That is the moment the evaluation ends and the negotiation begins.
About the Speakers
Devanathan Desikan AVP & AI Architect, Movate Devanathan works at the intersection of AI strategy and enterprise delivery, helping organizations build the infrastructure, governance, and orchestration needed to move AI beyond the lab. At Movate, he leads applied AI programs across industries, focused on the full journey from pilot to production.
Anju Chaudhary Vice President, Global Partnerships, Lyzr Anju leads global partnerships at Lyzr, an enterprise agentic AI platform built for production-grade deployments. She works with technology and services partners to help enterprises operationalize AI, bridging the gap between what looks good in a demo and what actually runs in the real world.
Date: 29th April
Time: 8:30 PM IST
