Overview
The insurance industry is undergoing a pivotal transformation, yet true AI autonomy remains out of reach. While a few leaders have successfully scaled AI, most insurers are still constrained by legacy infrastructure, regulatory caution and immature governance frameworks. Even among advanced adopters, fully delegating operations to autonomous AI agents is not yet feasible.
This paper explores two strategic paths forward: the long-term pursuit of artificial general intelligence (AGI) and the immediate application of reinforcement learning (RL). We introduce a novel reinforcement-switch framework which combines continuous learning with proactive human-AI control transfers to enable accountable autonomy. This model ensures resilience in dynamic environments by embedding trust, reversibility, and oversight into AI operations. It represents a fail-forward approach to engineering safe, scalable autonomy in insurance.
About the Authors
Dr. Venkatesh Upadrista, Global Head of Transformation, BFSI-IOA
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Dr. Venkatesh Upadrista leads global transformation for the BFSI-IOA vertical at Cognizant. In this role, he is responsible for driving AI-led transformation across the unit, ensuring customer success and enhancing the delivery of modern business operations within the financial services and insurance sectors.
Justin Slaten, Chief Information Officer, Venbrook Companies
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Justin Slaten is an accomplished technology executive with over 25 years of management experience. He is recognized for his innovative approach to product development, business process improvement, and scaling teams and operations. His leadership consistently drives enhanced productivity and sustainable growth.
Table of Contents
Background - Industry view
- The state of the industry - A fragmented landscape of maturity
- Autonomous AI agent control
- Two paths forward in solving the autonomy challenge
The AGI frontier - A long-term bet
The reinforcement-switch model with a pragmatic blueprint
- How the reinforcement-switch model works
References
