When AI agents become the 'customer' in insurance

Australia’s insurance sector is nearing a structural shift — not triggered by a new competitor or a regulation, but by consumers quietly outsourcing their first interaction to something non‑human. Across retail, travel and banking, personal AI agents already research, compare, filter and recommend; insurance is next.

These systems plan, reason and act towards a goal, executing tasks and decisions without constant human prompting — a step beyond simple chatbots or static comparison tools. As they mature, agents will take on the ‘heavy lifting’ people once did: evaluating policies, scanning exclusions, comparing price structures and optimising for value.

This flips the competitive script because AI agents don’t behave like traditional customers. They don’t respond to brand campaigns or heritage logos, and they don’t overlook complexity.

Instead, they interrogate structure — risk models, exclusions, claims performance, fees and long‑term value — at machine speed, prioritising transparency and consistency across datasets, not slogans. In practice, the ‘first decision’ in the funnel could be made by an algorithm long before a human taps ‘Get a quote’.

Why this shift matters in Australia now

This evolution arrives at a moment of significant challenge for Australian insurance. Cost‑of‑living pressures remain elevated and climate‑driven events are reshaping the risk landscape with growing frequency. Home insurance premiums have increased by 51% in the past five years, particularly in regions exposed to bushfires and floods, with affordability pressure intensifying across several states.

At the same time, Australian consumers are increasingly digital. They expect seamless, transparent experiences — and a growing share are using AI tools to navigate complex decisions. In such an environment, personal AI agents amplify these expectations: they will not tolerate unclear policy terms, opaque pricing or inconsistent data models.

This shift is not theoretical; we are already seeing Australian insurers adapt. Suncorp, for example, has progressed from experimentation to enterprise‑scale AI deployment. It now uses AI across underwriting, natural disaster preparation and claims management, with agent‑like systems predicting claims, triggering proactive responses and processing thousands of straightforward claims automatically during events such as storms and cyclone seasons. The insurer has also introduced AI‑assisted contact‑centre tools, sentiment analytics and predictive triage, supported by a cloud‑modernised core and a new policy administration system that improves data quality and reduces friction across channels.

These are architectural changes — the kind that make insurers more legible to people and to algorithms.

The silent trade‑off behind AI convenience

As personal AI agents take on more research, comparison and decision-making, risks extend beyond traditional privacy and cybersecurity. These systems interpret complex insurance information and act on users’ behalf, raising concerns around fairness, consent, accountability and suitability.

A growing trust paradox deepens the issue: while many consumers are comfortable sharing data with AI, most remain concerned about privacy, misuse and transparency. This exposes vulnerabilities in data protection, identity security, biased or unsuitable recommendations, misinterpreted exclusions and liability — especially in Life and Annuities, where poor recommendations may cross into regulated advice.

When the customer is an algorithm: the hidden risks for insurers

For insurers, the rise of personal AI agents creates significant organisational risk. Machine-mediated journeys weaken direct customer relationships, obscure intent and limit data visibility. Carriers may face contractual ambiguity, adversarial agent behaviour, regulatory exposure and margin compression as agents aggregate demand, and reputational damage when errors occur in agent-to-insurer interactions.

These risks intensify as AI adoption grows among the Gen X and Boomer segments — key decision-makers for life, retirement and group coverage. The competitive landscape is shifting, requiring insurers to manage information asymmetry, suitability concerns and evolving compliance obligations while engaging autonomous systems that increasingly act as customers themselves.

Staying visible in an AI-driven insurance market

As personal AI agents mediate more buying decisions, machine-readable clarity becomes a competitive edge. According to IBM’s ‘Personal AI Agents – Their Role in the Digital Economy and How to Maintain Control’ white paper, insurers that provide structured product data, interoperable APIs, transparent pricing and AI-enabled operating models will be best positioned to integrate into agent-driven ecosystems. Success will depend not only on influencing customers, but on remaining selectable by the algorithms acting on their behalf.

Building trust in an AI-mediated market

Operating safely in this environment requires secure, auditable digital identity and consent frameworks for quote-and-bind, claims and servicing. Insurers will also need stronger governance tailored to agentic models — with clear rules on delegation, suitability, advice boundaries and liability, particularly in Life and Annuities. Transparency and explainability will matter as much to algorithms as to regulators and customers.

Change will be gradual: agents will move from research and shortlisting to autonomous transactions within defined parameters. By the time the shift is obvious, leaders and laggards will already be clear. Insurers investing now in governed agentic capabilities and AI-ready operations will be the ones preferred by the algorithms shaping future demand.

Image credit: iStock.com/Poca Wander Stock