Insurance, at its core, is information processing at scale. A policy is a structured set of facts that define who is covered, against which risks, under what conditions, and within what limits and exclusions. The industry’s work is to read that information and interpret it against the rules that govern it. Most of that work is still done by hand, with institutional knowledge, inside fragmented legacy systems. That is what makes insurance uniquely situated for AI transformation. The opportunity is not to digitize that work; it’s for AI to understand the systems, data, workflows, and decisions deeply enough to automate it responsibly.
The workload problem is structural
While the market is softening, that does not mean work becomes easier for agencies and brokers. Softer pricing drives more shopping and more remarketing, meaning the volume of work goes up even as rates come down. The risk environment behind it is not easing either. Natural catastrophes generated $107 billion in insured losses across 190 events in 2025, according to the Swiss Re Institute, and the U.S. Treasury Department has warned that home insurance is becoming more costly and harder to obtain in many communities. More of that pressure turns into phone calls, coverage questions, and remarketing, and it lands on the account manager.
At the same time, the talent available to absorb that work is getting harder to find. Deloitte’s 2026global insurance outlook notes that veteran employees are steadily leaving the workforce, that recruiting is not keeping pace, and that insurers must preserve hard-won knowledge while reskilling teams for a more digital future. More work is flowing into the system, while the experienced talent to handle it is getting harder to replace.
Adoption is not the same as impact
While historically cautious to adopt new technology, the insurance industry has moved relatively quickly here. BCG found that insurers moved quickly on early adoption across predictive AI, generative AI, and AI agents. Yet only 7% of surveyed insurance companies had successfully scaled AI across the organization, and about two-thirds had not moved past the pilot stage.
The problem is that most of those deployments still depend on a person to drive every step. They help that person work faster, but the work still depends on them. In high-value, complex situations, keeping a human in the loop is the right design. In high-volume servicing work, a model that required human review at every stage does almost nothing to address the underlying capacity issue.
The more pressing issue is data, not workflow
The common mistake is treating insurance operations as a workflow problem. On the surface, the tasks look simple: compare two policies, identify coverage gaps, source an alternative quote, and draft a note to a client. Underneath, each of those tasks is shaped by carrier-specific rules, state regulations, ZIP-code-level constraints, policy forms, endorsements, underwriting appetite, and the context of the individual client.
That context is not something a new hire walks in with. A strong account manager builds it over years, one carrier quirk and one declined risk at a time, until applying it becomes second nature. The work looks simple from the outside because experienced people make it look simple. What they are actually doing is drawing on knowledge that took years to accumulate, and that is what AI has to replicate. A system cannot responsibly recommend a coverage change unless it knows whether that coverage is even available from that carrier, in that state, for that risk. Fluency is not the same as accuracy, and only accuracy is safe to act on.
The industry’s data foundation is still catching up to that standard. AI can only apply that context if it can reach the data the context lives in, and most of that data is locked in formats and systems that were never built to be read by anything other than a person. ACORD’s 2025 Insurance Digital Maturity Study found that only a quarter of top insurers had truly digitized their value chain, while more than half were still working out how digitalization applies to their business at all. The data an AI system would need to act accurately is sitting in PDFs, carrier portals, and agency management systems that do not talk to each other. Independent Agent reported that 87% of agents would write more business with carriers that put real-time appetite and quoting inside their agency management systems, which is the same connectivity gap seen from the agent’s side of the desk. For AI, that gap is the difference between a system that can act and one that can only guess.
Trust is what decides adoption
Insurance is a trust business. Clients depend on it during some of the hardest moments of their lives, and regulators are right to scrutinize anything that affects pricing, coverage, claims, and availability. Any AI that touches those areas has to be accurate, explainable, and properly governed.
Regulators are already saying as much. The National Association of Insurance Commissioners model bulletin on AI use notes that AI can improve service and promote accuracy, and that it can also introduce risks including inaccuracy, unfair discrimination, data vulnerability, and a lack of transparency. The National Institute of Standards and Technology AI Risk Management Frameworkmakes a similar point about building trustworthiness into how AI systems are designed, developed, used, and evaluated.
For the agency leaders making these decisions, accuracy is the whole strategy. A team will only hand work to AI when it can see why a recommendation was made, trace it back to the source, override it when it needs to, and trust that the system will flag an exception instead of guessing. The more autonomous the workflow becomes, the more those controls matter.
What the next phase looks like
The next phase of AI in insurance does more than answer questions; it works across the operating model. It reads policies, identifies what changed, checks for coverage gaps, weighs market options, prepares client communications, and routes the exceptions to the right person.
None of this removes the insurance professional from the equation. It changes where their time goes. Instead of moving data between systems and reviewing every routine policy by hand, account managers spend their time on judgment, advice, relationship management, and the clients who actually need them. Automating the routine work makes proactive, human service affordable at a scale most teams cannot reach today.
There is a market reason to get there, not only an operational one. McKinsey has argued that AI is already changing consumer expectations, with customers increasingly expecting higher accuracy, reliability, personalization, and on-demand interactions. An agency that can deliver that level of attention across its whole book, not only its largest accounts, is the one positioned to keep clients as the market keeps shopping.
Almost everyone in this industry has adopted AI by now. What will separate the firms that pull ahead is depth. An agency can trust a system with real work only when that system understands insurance the way an experienced account manager does: the carrier rules, the state regulations, the appetite, the context behind each policy. That kind of accuracy is hard to build, and it is what makes a system safe to act on.
Surface-level tools can make a person faster, but a system with real domain depth can do the work itself. When that becomes possible across a book, capacity stops being a function of how many people an agency can hire, which is the constraint that has defined this industry for as long as it has existed.
