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Artificial intelligence (AI) is helping insurance companies improve sales, reduce customer onboarding costs and process claims more accurately, according to a report by McKinsey.
The report said insurers adopting a domain-based AI strategy have recorded a 10-20 per cent improvement in new-agent success rates and sales conversion rates, along with a 10-15 per cent increase in premium growth. Customer onboarding costs have declined by 20-40 per cent, while claims processing accuracy has improved by 3-5 per cent. McKinsey said insurers are focusing on applying AI across core business functions such as sales and distribution, underwriting, claims and policy servicing. Rather than deploying AI across the entire organisation at once, leading insurers are revamping specific business functions to generate measurable outcomes. "Leading insurers don't see AI as just another efficiency tool--they recognize it as a fundamental driver of transformation and an opportunity to improve growth, relationships with customers, and productivity," the report said. It added, "The transformation must be rooted in business value and the results have to be measurable." According to the report, insurers need to prioritise a few high-impact business areas and link AI adoption to measurable operational improvements such as reducing customer churn. McKinsey also said building in-house digital capabilities remains critical, with leading insurers aiming for 70-80 per cent of their digital talent to be employed internally while strengthening expertise to work alongside AI agents. The report highlighted that strong data infrastructure and reusable AI systems are essential for scaling AI across insurance operations. "Insurers excelling in AI rely on a flexible AI capabilities stack powered by reusable multiagent systems. The modern AI tech stack for an insurer is highly modular and flexible to cope with fast-changing technology," McKinsey said. It further noted, "Reuse of underlying AI components and capabilities is critical, as is an agentic AI mesh architecture. This composable, distributed, and vendor-agnostic architectural paradigm enables multiple agents to reason, collaborate, and act autonomously across an array of systems, tools, and language models securely and at scale." The report also stressed that successful AI adoption requires as much investment in organisational change as in technology. "Adoption is just as important as development. As a rule, for every dollar spent on developing digital and AI solutions, plan to spend at least another dollar to ensure full user adoption and scaling across the enterprise. Change management is the key differentiator between AI sitting idle and AI transforming operations," McKinsey said. (ANI)
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