Kannan Amaresh
Senior Vice President and
Head of Global Insurance, Infosys
How to use AI effectively is a particular challenge for the insurance industry. Insurance is built on long-established processes, risk controls, actuarial judgment, and regulatory trust. Meanwhile, AI adoption rates have outpaced personal computers, the internet and smartphones. Integrating the fast new technology into this seasoned, structured industry requires deep understanding of both.
Read furtherKannan Amaresh
Senior Vice President and
Head of Global Insurance, Infosys
Kannan Amaresh, Global Head of Insurance at Infosys, explains how insurers can move AI from promising pilots to measurable, enterprise-wide value.
Insurers have moved quickly to put AI to work. They have genuine motivations. As risks grow more complex and customer expectations rise, insurers must modernize data, governance, regulatory compliance, operating models, and workforce capabilities to turn promising pilots into trusted enterprise transformation.
Read furtherAI has become impossible for insurers to ignore — but widespread experimentation has not yet translated into enterprise transformation. As pilots multiply, insurers face mounting pressure to overcome fragmented data, legacy systems, unclear governance, and workforce resistance before AI can deliver measurable business value. The companies pulling ahead treat AI not as a collection of tools, but as a disciplined operating model built on shared platforms, responsible governance, workforce engagement, and clear outcomes.
AI is poised to reshape insurance as climate volatility, aging populations, cyber risk, and changing customer expectations make traditional models harder to sustain. Insurers that can turn real-time data into transparent, explainable decisions will move from reactive claims handling to proactive risk prevention, continuous coverage, and faster, more personalized service. But success will depend on more than technology: leaders must build trusted AI foundations now to prepare their data, workforce, and governance for an agentic future.
Fragmented data, legacy systems, and undocumented institutional knowledge limit insurers’ ability to leverage artificial intelligence. Insurance companies must learn to capture data, processes, and human expertise in ways that make AI agents useful. Done properly and with urgency, insurers can convert modernization from a technical obligation into a competitive advantage.
Insurers are using AI to speed up workflows and automate decisions. Risks associated with AI adoption are advancing just as quickly. As insurers experiment with generative and agentic AI, regulators are racing to address bias, vendor concentration, cyber exposure, and the growing question of who is accountable when machines act with human-like judgment. This article describes how insurers can govern AI with enough rigor to preserve trust, while still benefitting from the efficiency and innovation AI offers.
As AI adoption accelerates and costs rise, carriers need to show whether AI is worth it. Insurers can frame the AI ROI question with three outcomes that define insurance economics: how customers buy, how risk is priced, and how claims are settled. This article explains how a practical value framework can help insurers separate scalable advantage from expensive experiments.
