Insurance is having its Stripe moment. AI is the reason.

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The U.S. insurance industry is enormous, and it is still learning how to use its own data. According to NAIC data, carriers collected roughly $1.1 trillion in property and casualty premiums in 2025, while in another NAIC report, life and annuity segments added another $1.5 trillion in direct written premiums. Yet most companies make decisions months after the facts that should drive them. The gap between what the industry knows and when it acts on it is the quiet problem behind every headline about premiums, claims, and customer churn. 

The scale of the data problem is hard to overstate. Insurance generates a huge amount of information across the selling, procurement, and servicing process. Every web visit, phone call, quote request, and policy document adds to the pile, and agencies, carriers, and brokers each hold pieces of the same customer story. Manual processes still dominate, with fax machines, letters, and email in daily use, and the picture is usually out of date by the time it is assembled. 

Artificial intelligence is the tool most executives reach for, and it often disappoints. Many have been told by their teams that they must use AI, but they do not know what it should do, how to deploy it, or how to answer the security, privacy, and infrastructure questions that come with it. Some fall back on the oldest rule in business: if it is not broken, do not fix it. The result is a market full of pilots and caution, with only a few companies turning AI into real operations. 

The insurers that make progress focus on three changes. First, they treat data collection and ingestion as a system problem, not a side project. Second, they compress the speed of process, moving information and decisions from days to minutes. Third, they build a more customized consumer journey. Insurance lags far behind the recommendation engines of consumer goods and online retail. Customers still get blanket offers for products they do not need, and carriers rarely see the early signals that a policyholder is shopping, struggling, or at risk of leaving. 

Tyler Rees, founder and CEO of EnrollHere, has built his company around that missing layer. EnrollHere, an AI-powered platform and system of record for insurance distribution, unifies the fragments that agencies, field marketing organizations, and carriers manage separately. The platform connects marketing, call-center infrastructure, agent activity, compliance oversight, policy lifecycle management, commissions, and financial payments into a single operating environment.  

Rees argues that AI is the ingestion point, not the answer by itself. “AI can ingest a lot of data,” he says, “but if a human does not know how to inform what metrics matter and what they mean, then it is useless.” Humans still handle oversight, management, and interpretation of what the data shows, through visibility dashboards, fact-checking, and subject matter experts who translate signals into action. EnrollHere is more of a service than a platform, and that distinction matters when a carrier has to trust the numbers.

The payoff shows up in the speed of decisions. In insurance, many choices are still made six to nine months after the events that should drive them. A customer starts shopping, a policy lapses, a complaint forms, and the company finds out later, when the outcome is harder to change. AI can surface those signals in real time. Instead of discovering the problem at renewal, a broker can see a shift in behavior the same week it happens. That velocity changes what is possible for carriers, agents, and families. 

Cost follows speed. Carriers and brokers spend far more than they need to because they cannot tell which leads are worth pursuing. They pour money into campaigns that reach the wrong consumers, and some of that targeting borders on predatory, steering people toward products that do not fit. AI helps on both sides: it identifies the leads most likely to convert, and it helps companies stop spending on the ones that never will. For the consumer, the result is a smaller flow of irrelevant offers. 

The industry has been here before, in another disguise. Ten years ago, payment processing worked with checks, debit cards, and slow settlement. Then Stripe and Square rebuilt the rails, and the whole sector moved forward in a few years. Insurance sits at a similar crossroads. The data exists, the tools exist, and customers expect more. The question is whether carriers and brokers will treat AI as a real operating layer or leave it as a pilot that never ships. 

The winners of the next decade will close the gap between insight and action. McKinsey’s analysis of insurance AI, as MoneyGeek reported this year, found AI-leading insurers generated 6.1 times the total shareholder return of AI laggards, a spread wider than in most industries. Deloitte finds P&C insurers could save up to $160 billion by 2032 through AI-driven fraud analytics alone. The pieces are on the table. What is missing, in most firms, is the decision to put them together.