In a recent analysis, McKinsey & Company said the global insurance industry faces growing pressure from artificial intelligence, as the technology begins to reshape long-standing challenges around growth, distribution costs, and productivity. The firm estimates that gross written premiums reached approximately $8.3 trillion in 2025, with profits before tax hitting $580 billion, but rising capital requirements have held profit growth to an annual rate of 4.3% since 2005, compared with 4.9% annual premium growth.
The insurance sector has historically resisted the kind of disruption that digitalization and platform-based models brought to other industries. Competitive positions shift slowly, capital moves across regions and business lines at a measured pace, and public markets continue to treat insurance as a stable earnings generator. McKinsey noted that private capital has introduced innovation in balance sheet management and investment strategies, but the broader value chain has seen less change. The firm said the industry of 2026 would still look familiar to executives who saw it in 2006.
Protection gaps and declining relevance
McKinsey identified a widening gap between global risk exposure and the industry's ability to cover it. Insurance revenues have grown more slowly than many major industries and GDP. Personal lines represented 1% of global GDP in 2023, down from 1.2% in 2019. Growth in developed markets has often come from pricing increases, not from extending coverage into new areas of risk.
The protection gap is stark in emerging risks. The global natural catastrophe protection gap reached $133 billion in 2025, and less than 1% of global cyber costs are insured-a potential gap of around $900 billion. The firm argued that insurance is becoming less aligned with an increasingly complex risk environment.
How AI could reshape the sector
AI could open new opportunities through insurable risks that did not exist before, including AI liability, non-physical business interruption, and workforce-related risks tied to AI adoption. Technologies such as parametric insurance, embedded micro-coverage, and real-time data-driven policies could make insurance accessible to customers and risks that have been difficult to serve economically.
The analysis also points to a shift from traditional risk transfer toward broader risk partnerships. Conventional insurance focuses on responding after losses, but AI could enable continuous monitoring, prevention insights, and early intervention. Examples include telematics systems that adjust premiums while giving real-time driving guidance, risk management tools backed by satellite and IoT data, and AI-enabled health support designed to improve outcomes. These approaches exist today but have not yet become central to the insurance proposition.
McKinsey said AI could improve access to markets by strengthening underwriting and claims. Emerging risks such as climate-related property exposure, cyber threats, and AI-related liability remain hard to price because insurers lack reliable data and predictive confidence. Improved data analysis, continuous model updates, and more accurate claims assessment could help carriers price risk effectively and expand coverage. The firm noted that insurers able to build these capabilities early may gain advantages through better loss ratios, stronger pricing confidence, and the ability to enter markets where competitors remain cautious. The topic of AI for Insurance is drawing attention as professionals explore how the technology can sharpen underwriting and risk assessment.
Challenges ahead
Not all digital risks behave like traditional insurance exposures. Shared infrastructure, interconnected supply chains, and common technology dependencies could produce highly correlated losses. Insurers entering these areas will need strong analytical capabilities to understand how risks develop and spread, not just create new products, McKinsey said.
Why this matters for insurance professionals
Insurers and distributors that build AI capabilities for underwriting, risk monitoring, and product innovation may be better positioned as the industry confronts growing protection gaps and new risk categories. The shift from simple risk transfer to prevention-focused partnerships could alter valuation models and competitive dynamics. Professionals should focus on developing data-driven skills and understanding how AI can expand insurability in emerging areas-particularly in cyber, climate, and AI liability-where coverage is currently thin and pricing models are still maturing.
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