Wednesday, January 28th at 11:00 AM US ET (5:00 PM CET)
Many teams are running GenAI pilots, but few can show clear ROI or account for rising AI usage costs. The biggest blockers aren’t the LLMs; they’re choosing the right problems (often unglamorous back-office tasks), defining success upfront, and keeping a handle on unit costs as you scale.
Join the live session as Senior AI/ML specialists Rupal Bhatt and Eduardo Mota share proven architectural and adoption patterns drawn from dozens of real-world GenAI deployments they’ve worked on. They will walk through a practical playbook for evaluating use cases and aligning cost with value from pilot to production.
In this session we'll explore how to:
Prioritize the right use cases by defining clear success KPIs before you build
Baseline and track GenAI ROI across time, cost, quality, and throughput
Design POCs with FinOps guardrails to track cost-per-outcome from day one
This session is for:
Product, Engineering, and Data leaders responsible for delivering and proving GenAI value
FinOps leaders managing, allocating, and optimizing AI/GenAI costs
Rupal is a GenAI and cloud strategist specializing in turning AI experimentation into measurable business outcomes. With AWS Specialty certifications in Machine Learning, Data Analytics, and Databases, she brings a strong technical foundation to real-world GenAI deployments across fintech, healthcare, and marketing technology. At DoiT, Rupal has helped dozens of teams scope, prototype, and operationalize GenAI use cases that streamline operations and accelerate product development.
Eduardo is a Senior Cloud Data Architect with deep experience designing and optimizing GenAI and machine learning workloads on AWS. He has led numerous GenAI deployments across industries, helping teams improve performance, reduce cost, and move from pilot to production with operational reliability. Eduardo holds multiple AWS certifications in Machine Learning and Data Analytics, bringing a practical, results-driven perspective to scaling AI in the enterprise.
