Insurance Agent Productivity Could Rise by 20% with Wider AI Use

Staff Correspondent: Artificial intelligence stands ready to reshape the economics of the global insurance industry by cutting costs, altering the way policies reach customers and enabling coverage of risks that have long proved difficult to price, according to a report from McKinsey & Company.

Between 2005 and 2025 worldwide premiums expanded at roughly 4.9 percent a year to reach an estimated 8.3 trillion dollars, yet pre-tax profits advanced more slowly at about 4.3 percent a year to around 580 billion dollars. The sector has also trailed other industries in both revenue growth and cost control, with operating expenses as a share of revenue climbing 10 percent globally and 22 percent in North America over the same two decades.

McKinsey estimates that AI tools can already reduce customer onboarding costs by between 20 and 40 percent while lifting agent productivity by 10 to 20 percent. The technology promises further gains in underwriting accuracy, claims handling and pricing precision, opening the door to better coverage of cyber exposures, climate related losses and emerging liabilities tied to artificial intelligence itself.

At present less than one percent of global cyber costs are insured, leaving a protection gap of approximately 900 billion dollars, while the natural catastrophe protection gap stood at 133 billion dollars in 2025.

Distribution patterns remain heavily intermediated, with agents, brokers and managing general agents accounting for 85 percent of United States property and casualty premiums and 95 percent of life insurance premiums.

AI assistants may increasingly take on roles such as comparing coverages, tracking renewal dates and recommending providers, especially for simpler products. At the same time the report cautions that AI related and cyber risks can be tightly interconnected, complicating efforts to diversify and price losses.

Insurers face a strategic choice between competing through scale and cost efficiency or through deep specialisation. Success will require sustained investment in data architecture and technology along with fundamental changes to operating models.

Firms that treat AI merely as a gradual upgrade risk being left behind by competitors who embed the technology across underwriting, distribution and claims processes.