There is a discipline that has dominated insurance product design for the last two decades: user experience. We mapped customer journeys. We eliminated friction. We A/B tested copy. We moved the button above the fold and measured the conversion lift. We hired UX researchers and ran workshops and iterated on prototypes. All of it premised on one assumption: that there was a human on the other side of the screen, making decisions.
That assumption is becoming incomplete.
The next buyer of your insurance product might not be a person navigating a journey you designed. It might be an AI agent, acting on behalf of one, collecting requirements, comparing coverage terms, flagging exclusions, and making a recommendation before a human ever reads a policy document.
An agent does not care about your UX. It cares about your data structure.
What an AI agent actually does
The term "agentic AI" is used loosely, but it has a specific meaning in the context of insurance distribution. An AI agent is not a chatbot that answers questions. It is a system that can take actions: searching for products, collecting information, generating quotes, evaluating terms, and, in some configurations, completing a purchase. It operates on behalf of a user, often without that user being actively present at each step.
Gartner predicted in 2025 that by 2026, up to 40 per cent of enterprise applications would include task-specific AI agents, up from less than five per cent in 2025. Insurance industry analysts at InsuranceNewsNet reported in late 2025 that half of all US insurance consumers were expected to use AI tools to research and shop for insurance policies in 2026. SAS data from a trust survey conducted with IDC showed that insurance decision-makers trust generative AI 100 per cent more than machine learning tools, a finding that surprised most who saw it.
Agentic AI in insurance is not a future scenario being stress-tested in labs. It is early-stage production reality. Zurich validated the benefits of AI agents in underwriting after six months of deployment. Markel has publicly discussed AI agents in data ingestion. BCG, writing in early 2026 on the AI-first P&C insurer, described the transition in stark terms: autonomous AI agents becoming the primary execution engine for core insurance workflows, with humans engaged for oversight and exception handling rather than as the default processors of every transaction.
The practical implication for product design
When a human browses for insurance, they navigate. They click, read, scroll back, ask a question, leave and return. The interface absorbs ambiguity. A skilled broker absorbs even more of it, asking clarifying questions, interpreting the client's situation, translating needs into coverage.
When an agent looks for insurance, it queries. It needs structured, unambiguous, machine-readable information. It needs to know what a product covers and what it excludes, expressed in a format it can evaluate and compare. It needs a real-time API endpoint that returns a rated quote when given a set of input parameters. It needs a response it can act on.
This creates a specific design challenge for insurance products. Most policy wordings are written for human readers, lawyers and judges, ultimately, but at least human interpreters. Most product pages are built for human navigators. Most quote flows are built for human form-fillers. None of those surfaces are optimised for machine consumption.
The businesses that understand this earliest will have a structural advantage in distribution. Not because they built a ChatGPT app, but because they made their products readable to the systems that are increasingly doing the reading.
Three things that need to change
Data structure. The way an insurance product is described, its coverage categories, its exclusions, its eligibility criteria, its pricing inputs, needs to be structured in a way that a machine can parse. This is not the same as having a good website. It means making the core product logic accessible in a standardised format. ACORD standards exist for a reason; the question is whether your systems actually use them end-to-end, or whether they are a compliance checkbox attached to a legacy data model.
An agent evaluating two competing business pack policies does not read marketing copy. It reads structured coverage data. If your product description is buried in a 40-page PDF that requires human interpretation, it will not compare well, or compare at all.
API architecture. For an AI agent to quote a policy on behalf of a buyer, it needs real-time access to your rating engine. Not a form. Not a portal. Not an email to an underwriter. A callable API endpoint that accepts structured inputs and returns a rated quote in under a second. This is the infrastructure precondition for AI distribution, and it is the capability that separated the insurers who could launch ChatGPT apps in February 2026 from those who could not.
The 2024 Insurance API Index reported that 86 carriers were offering API-enabled products, and by mid-2025 more than 75 per cent of insurance firms had embedded APIs into their operations. But most of those APIs are internal integration points, not open distribution endpoints. The capability gap between "we have APIs" and "an AI agent can quote our product in real time" is significant.
Explainability. AI agents make recommendations. To make a recommendation, they need to be able to explain the basis for it, in terms that the downstream AI (and ultimately the human it is representing) can evaluate. Insurance products that rely on complexity as a differentiation strategy, products where the value is embedded in detailed policy conditions that require expert interpretation, are poorly positioned for AI-mediated distribution.
This does not mean products should be dumbed down. It means the logic of the product, what it covers, what it excludes, why that matters, needs to be expressible in a form that can travel cleanly through a machine-to-machine exchange. Explainability is a product design challenge, not just an AI governance challenge.
The specialisation advantage
There is a genuine advantage available to specialist insurers and MGAs here, but it is not the one most people assume.
The instinct is to treat AI distribution as a threat to specialists: commoditised AI will surface the cheapest product, margins will compress, and the nuance that distinguishes a good specialist product from a generic one will be invisible to a machine. That risk is real for the bottom half of the specialty market, products that claim to be specialist but are functionally commodities sold at a premium.
But genuine specialists, businesses that write risks that require real underwriting judgment, that have loss ratios others cannot match, that understand a class of business more deeply than any AI will in the near term, have a durable position. The reason AI apps are starting with home insurance and personal motor is not coincidence. These are the simplest products to structure for machine consumption, with the most standardised coverage language and the most price-sensitive buyers. Niche commercial lines, professional indemnity for unusual occupations, specialty liability, these are categorically harder problems for an AI agent to solve.
The catch is that this advantage only holds if specialists make their products as machine-readable as possible within their class. An AI agent that cannot parse your product will not recommend it, regardless of how good it is. Being invisible to machines is not a moat. It is a liability.
What to do with this now
The practical starting point is not a technology project. It is an audit.
Ask your team: if an AI agent, acting on behalf of a small business owner looking for the insurance your business writes, sent a structured query to our systems today, what would happen? Could we respond with a rated quote? In what timeframe? In what format?
For most Australian insurers and MGAs, the honest answer is: we could not. Not because the technology does not exist, but because the infrastructure was not built with that use case in mind. It was built for humans filling in forms.
The infrastructure decisions that need to be made are not immediate crisis items. The Australian market has more time than the US and European markets, where the distribution shift is already live. But infrastructure decisions have long lead times. The businesses starting those conversations now will be ready when the channel matures. The ones waiting for the channel to mature before starting will find themselves behind.
The interface is not disappearing tomorrow. But it is disappearing. Designing for the world that is coming, where AI agents are a meaningful part of the distribution chain, acting on behalf of buyers who may never directly interact with your systems, is no longer a speculative exercise. It is product strategy.
Sources: Gartner AI Agent forecast (2025), InsuranceNewsNet agentic AI survey (Dec 2025), BCG AI-First P&C Insurer (Jan 2026), ScienceSoft Q4 Insurance AI Trends (Jan 2026), Insurance API Index (2024), FinTech Global / Tuio launch (Feb 2026)