Shravankumar Chandrasekaran

Shravankumar Chandrasekaran is global product manager at Marsh McLennan.

He has over 13 years of experience across product management, software development, and insurance. He focuses on leveraging advanced analytics and AI to drive benchmarking solutions globally.

He received an M.S. in operations research from Columbia University and a B.Tech in electronics and communications engineering from Amrita Vishwa Vidyapeetham in Bangalore, India.

Most Recent

Insurance AI

As AI Spreads, Orchestration Is Required

By

Shravankumar Chandrasekaran

AI enables teams to build intelligence locally, but enterprise value depends on coordinating meaning across organizational boundaries.

All Articles

Man Using ChatGPT AI System

Thinking About AI Agents? Get Your Tools Right First

By

Alejandro Zarate Santovena

Shravankumar Chandrasekaran

MIT research reveals 95% of AI pilots fail to deliver value as enterprises skip crucial tool-building for flashier autonomous agents.

An artist’s illustration of artificial intelligence

Bridging the GenAI Divide

By

Shravankumar Chandrasekaran

95% of enterprise AI projects fail to translate massive investment into business value. Here are five strategies and five guidelines that can help.

artificial intelligence

A Data Strategy for Successful AI Adoption

By

Shravankumar Chandrasekaran

Alejandro Zarate Santovena

Despite significant investments in AI, many organizations struggle to derive measurable value.

Six Things

Insurance

A Scary Stat for Insurance Brands

By Paul Carroll

As AI search reshapes buying behaviors, Flo, Mayhem, and other big insurance brands likely will lose some of their sway.

Why Aren't Insurance Customers Feeling AI's Benefits?

Insurers deployed AI at record speed to boost internal efficiency, but customers see no difference—and that's the industry's biggest strategic vulnerability.

1

The New World of Accident Reconstruction

Digital evidence from dashcams, drones and vehicle data recorders now complements physical crash analysis in modern accident reconstruction.

2

Explainable AI Is Not Enough

You cannot explain your way out of algorithmic bias. You have to intercept it before the rate exists. 

3

A Hidden AI Risk: Passive Sabotage

Insurance leaders who wait for perfect AI can fall behind competitors already learning to deploy it strategically and responsibly.

4

Insurance Agentic AI Needs New Business Models

Asian insurers show that agentic AI success requires reimagining market forces, not just chasing productivity gains through isolated use cases.

5

As AI Spreads, Orchestration Is Required

AI enables teams to build intelligence locally, but enterprise value depends on coordinating meaning across organizational boundaries.

6

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As AI Spreads, Orchestration Is Required

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Insurance Agentic AI Needs New Business Models

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