Data to Decisions:
Building the AI-Ready Enterprise

A Closed-Door Roundtable for Retail & eCommerce Leaders

5:30 PM onwards

Registration Ended

Our Engagements

On This Roundtable

5+

Speakers

15+

Delegates

Overview

Your customers are already shopping with AI comparing, deciding, and buying through agents that don't care about your brand story. Agentic commerce isn't coming; it's here. And while your competitors race to build AI-powered experiences, the uncomfortable truth remains: most enterprise data stacks weren't built for this moment. You're sitting on petabytes of customer signals, supply chain intelligence, and transaction history but if your foundation can't feed an agent in real time, you're not in the game.

This roundtable brings together Retail and eCommerce leaders to have the real conversation: not about AI aspirations, but about the foundation underneath it. Because the difference between an AI strategy that ships and one that stalls isn't the model it's the data.

Shellkode and Snowflake are hosting a closed-door session to cut through the noise, benchmark where the industry actually stands, and figure out what it takes to build a data foundation that your AI can actually stand on."

Speakers:

Siddhesh S

Sr Solutions Architect

ShellKode

Manasa K

Head Data Practice

ShellKode

Mohit Jairath

Head of Growth Partners

Snowflake India

Harish Chintakunta

Senior Partner Solution Engineer

Snowflake India

Slishaa Shetty

Account Director

Snowflake India

Agenda:

Time Session
5:30 PM – 6:30 PM Registration & Guest Welcome
Arrivals, introductions, and pre-event networking
6:30 PM – 7:00 PM Setting the Stage
Shellkode and Snowflake on industry trends, challenges, and the real state of data readiness in enterprise retail
7:00 PM – 8:30 PM The Roundtable
A deep dive into the challenges and opportunities of implementing AI in retail
08:30 PM Onwards Networking Dinner & Drinks
Open floor conversations, connections, and follow-through

Glimpse of Data to Decisions: Building the AI-Ready Enterprise