The report said insurers will increasingly have to choose between competing through scale and cost or through deep specialisation, while building the technology, data and operating capabilities required to respond at “technology speed”. Companies that delay AI transformation could see the competitive gap widen, while those able to test, learn and scale faster may build a structural advantage.
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McKinsey said AI could help insurers expand into emerging risk pools, improve underwriting and claims accuracy and shift the industry from traditional risk transfer towards continuous, data-driven risk management. New areas such as AI liability, nonphysical business interruption and AI-augmented workforce transitions could expand the insurance market, while parametric products, embedded micro-coverage and on-demand policies could make previously uneconomic segments more accessible.
The report noted that the global insurance industry has struggled to translate premium growth into stronger profitability. Gross written premiums expanded at about 4.9 per cent annually between 2005 and 2025 to an estimated $8.3 trillion, while profits before tax grew by around 4.3 per cent to approximately $580 billion.
AI could also address the sector’s longstanding productivity challenge. McKinsey said insurance cost ratios have risen globally despite heavy investment in automation and digital tools. However, AI-driven transformations are already producing 20-40 per cent reductions in customer onboarding costs and 10-20 per cent improvements in insurance agent productivity in some domains.
Distribution could undergo a major shift as AI assistants increasingly compare coverage, prices and products and potentially influence customers’ purchase decisions. The report said the “front door” to insurance could increasingly become an AI assistant, platform or ecosystem rather than a traditional agent or insurer website.Also read: FinMin launches Protection & Indemnity Insurance product under Bharat Maritime Insurance Pool
McKinsey said insurers will need to build faster decision-making, stronger data architecture and closer integration between business and technical teams. AI leaders have already generated six times greater total shareholder returns than laggards, highlighting the potential financial consequences of moving quickly.