Insurers Are Ready to Hand Repeatable Work to AI, Just 6% Would Trust a General-Purpose Model to Do It

Research shows insurers are eager to expand AI in operations but demand governed, insurance-specific models and keep high-consequence decisions with humans.

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New research finds that 83% of the global insurance market would hand repeatable work to AI; 75% will only allow AI to execute the work with an insurance-specific or governed model. Less than 1% of the market runs fully AI-native operations today.

HAMILTON, Bermuda--(BUSINESS WIRE)-- The insurance market is ready to hand repeatable operational work to AI so it can win business it is currently turning away. A full 83% of the market supports AI executing repeatable work. A further majority, 75%, would only allow AI execution with a model built for insurance or governed inside their own rules. Both findings come from a report published today by ISG, the global technology research and advisory firm, and commissioned by mea Platform.

The study draws on senior leaders across underwriting, operations, claims, technology and transformation. It covers 20 operational activities, from submission intake and triage to quote generation, bordereaux processing, claims adjudication and compliance screening, across North America, Europe and Asia.

Consequential decisions, those that create true company distinction, should stay with people, according to 86% of respondents. Insurers want AI running the repeated work and sharpening the consequential work underneath it, so an underwriter or broker reaches a risk decision with better evidence in front of them and reaches it sooner.

Operations decide how much business reaches a decision

Carriers estimate that one in nine broker submissions is declined or left unquoted because operations cannot keep up, on risks they had the appetite to write.

Where AI is already running in operations, 61% report productivity improvements and 51% faster cycle time, with operating costs expected to fall 16% over two years. While speed and efficiency are major components of the appeal for AI-enabled systems, capacity becomes the bigger prize. Asked what would most improve how brokers see them, 64% named pricing, 52% ease of doing business and 51% AI-driven speed and completeness of submission response.

Trust runs through the model

Asked what they would trust for high-consequence underwriting and claims decisions made with limited oversight, 75% chose an insurance-specific model or a governed hybrid. Only 6% would accept a general-purpose model on its own.

Governed carries a specific meaning in the research. The system draws on controlled policy wording, endorsements, underwriting appetite and claims guidance. Its permissions separate what it may read from what it may decide. A named person can override it or stop it. And it runs on that institution's own appetite, thresholds and referral triggers, so the organization scales its existing practice.

“Insurers are ready to hand the repeated work to AI, and they will do it on one condition, which is a model that understands insurance and operates inside their own rules. That is a demanding standard and the right one, because the prize on the other side is growth. Carriers are turning away business they had the appetite to write because operations cannot keep up, and winning that back is worth more than any saving.”

Martin Henley, Chief Executive, mea Platform

Ambition at 96%, fully AI-native at less than 1%

Ninety-six percent of insurers have AI-led redesign on the agenda. Advanced operating posture, where AI is central to workflow design, stands at 13% today against 52% intended within two years. Fully AI-native operation, where AI executes defined processes end to end while people set policy and manage exceptions, sits below 1% today, and 12% expect to reach it within two years.

“Insurers know exactly where they want the line between AI and human judgment. Getting there is the part they have not solved. The gap between 96% with redesign on the agenda and the 13% that have reached an AI-centred operating model defines the next two years.”

Ashish Jhajharia, Insurance SME and Principal Analyst, ISG

AI-native, as the study defines it, means AI executing defined processes end to end while people set policy and manage exceptions. Almost no insurer operates that way today. The work that gets a firm there is operating-model work: decision rights, referral rules and an evidence trail that let a system act on the institution’s behalf. Insurers will only permit that with a model built for insurance and governed inside their own rules, which is why 27% of the market is still evaluating rather than building.

The report is available at https://www.meaplatform.com/state-of-insurance-operations/.

About mea Platform

mea is the insurance-native agentic AI company. Built by insurance operators, mea provides the operational infrastructure that insurers, brokers, MGAs, and reinsurers use to run their core workflows. Its product suite runs agentic AI across underwriting, claims, finance, and broking, powered by a proprietary domain-specific language model℠ (dsLM) and insurance knowledge graph℠. mea is live with more than 30 clients across 21 countries and has processed more than $450 billion in gross written premium.

About ISG

ISG (Nasdaq: III) is a global AI-centered technology research and advisory firm. A trusted partner to more than 900 clients, including 75 of the world’s top 100 enterprises, ISG is a long-time leader in technology and business services that is now at the forefront of leveraging AI to help organizations achieve operational excellence and faster growth. The firm, founded in 2006, is known for its proprietary market data and research, in-depth knowledge and governance of provider ecosystems, and the expertise of its 1,500 professionals worldwide working together to help clients maximize the value of their technology investments.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260916484880/en/

Media contacts

mea Platform. Elliott Bundy, Chief Marketing Officer. Elliott.Bundy@meaplatform.com. +1 (347) 561 0276

Source: mea Platform

Key Terms

ai-native technical

A company or product described as ai-native is built from the ground up around artificial intelligence rather than having AI added on later; its core processes, user experience and decision-making rely on machine learning models and data pipelines. Like a house designed for wheelchair access versus one retrofitted, ai-native firms can move faster, scale more cheaply and offer unique products or cost advantages—information investors use to judge future growth potential and risk exposure to model, data or regulatory failures.

claims adjudication technical

The process by which an insurer, government payer, or third-party administrator reviews, validates and decides whether to pay, deny, or adjust a claim for benefits or medical services. It matters to investors because how accurately and efficiently claims are adjudicated affects an insurer’s cash flow, reserves, profit margins and regulatory compliance—similar to how a cashier checking receipts determines whether a store owes or keeps money.

bordereaux processing technical

Bordereaux processing is the routine handling and reconciliation of detailed reports (called bordereaux) that list premiums, claims, policies or exposures transferred between insurers and reinsurers or between different business units. It turns those raw lists into validated, coded records for accounting, billing and risk tracking—much like turning a stack of sales invoices into a cleaned, posted ledger—so investors can rely on reported revenue, liabilities and cash flows being accurate.