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Ananya Ghosh Chowdhury
Ananya Ghosh Chowdhury

Ananya Ghosh Chowdhury

Senior Cloud Solution Architect
Microsoft
Bio

Ananya Ghosh Chowdhury is a Senior Cloud Solution Architect at Microsoft, specializing in enterprise-scale analytics, artificial intelligence, and decision-centric system design. She works at the intersection of advanced analytics, AI, and business strategy, helping global organizations translate complex data ecosystems into actionable decisions with measurable impact.

With deep experience across cloud architecture, predictive analytics, generative AI, and data platforms such as Microsoft Fabric, Azure AI Foundry, and Databricks, Ananya has led and advised transformation initiatives across retail, consumer goods, operations, and supply chain domains. Her work focuses not just on building analytics solutions, but on enabling leaders to reason better, decide faster, and align analytics investments with real-world outcomes.

Ananya is an IEEE Senior Member, published researcher, author of an upcoming book, and a frequent speaker at industry and academic conferences. She has spoken at events such as ODSC, Microsoft executive briefings, and international analytics forums, and actively mentors early‑career professionals and women in technology.

She is passionate about reframing analytics as a decision discipline—not just a technical function—and bringing clarity, structure, and human judgment back into data-driven organizations.


Agentic Analytics: How AI Agents Turn Insights into Decisions

As analytics systems become more advanced, organizations face a familiar challenge: abundant insights, but limited action. Traditional dashboards, predictive models, and even generative AI tools often stop short of supporting end‑to‑end decision‑making. This session introduces agentic analytics—a new paradigm in which AI agents move beyond insight generation to actively support decision workflows.

Agentic analytics combines generative AI, predictive models, and decision logic to create systems that can reason about context, evaluate alternatives, coordinate actions, and adapt to changing conditions. Rather than answering isolated questions, AI agents operate across the analytics lifecycle—framing problems, retrieving and synthesizing data, surfacing tradeoffs, and enabling timely, decision‑ready recommendations.

Drawing on real‑world enterprise scenarios, this talk explores how agentic analytics differs from traditional analytics and earlier generations of AI. Attendees will learn when agent‑based approaches add value, how to design AI agents that balance automation with human judgment, and what architectural and governance considerations are essential for trustworthy deployment. Topics include orchestration across multiple models and tools, grounding AI agents in enterprise data, and managing uncertainty in high‑stakes decisions.

This session is designed for analytics practitioners, leaders, and researchers interested in emerging analytics methods that bridge the gap between insight and action—helping organizations move from knowing to deciding.

Professional / Leadership
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