Srivathsan Karanai Margan

Organizations rushing to deploy AI face a widening gap between pilot success and production value, driven by persistent misconceptions about capability versus application.

Autonomous AI systems compress time, amplify reach, and create unfamiliar loss pathways that traditional insurance coverages cannot address.

As AI shifts to autonomous decision-making across insurance, traditional governance cannot identify or control the resulting systemic risks.

As AI shifts from advisory tool to autonomous decision-maker, insurers face new risk categories that traditional frameworks weren't designed to address.

Legacy infrastructure systems face critical tolerance thresholds as climate change and urbanization exceed original design parameters.

Baby Boomers are suffering from a catastrophic shift of pension risks from institutions to individuals, because of a misunderstanding of behavioral steering. 

The insurance industry is shifting from traditional actuarial models to incorporate behavioral data in risk assessment practices. The implications are enormous.

Proponents rave that embedded insurance benefits the insurer, third parties and customers, but we should temper the hype--without spoiling the enthusiasm.