TL;DR: AI disruption is real and happening today. Insurance organisations need to move quickly to take advantage of this early window of opportunity to emerge as leaders in the coming decade. This blog presents five key AI impacts on the sector and what insurance organisations can do to leverage the opportunity.
Adopting AI while maintaining the insurance sector's rigorous compliance standards is hard enough. But long-standing brands must do so while operating within legacy systems and tightly controlled operational frameworks.
Some leading insurance organisations are cracking the AI code. A V2 AI client identified an opportunity in their quotes and claims processing workflows. The client’s existing process required coordination across several teams to securely analyse customer documentation and prepare a response. Depending on the request's complexity, these processes could take anywhere from days to weeks.
AI was used to reduce request processing time by 60%, increasing the likelihood that submitted quotes progress to bind and strengthening our clients’ position in a competitive, broker-driven market.
Here is what insurance leaders are aware of about AI and what they are getting right!
#1 AI is Influencing What Customers Expect from Insurance
AI raises the bar on insurance affordability, personalisation, and trust.
Customers today are under increasing financial pressure as rising living costs coincide with sustained premium increases. At the same time, lifestyle shifts are impacting the type and scope of insurance coverage required. Instead of automatic renewals, many reassess the value of insurance, downgrade, or forgo coverage entirely.
To be successful, insurance products today have to be affordable, personalised, and aligned to customers’ real-world risk profiles. AI can help insurers design and deliver these service shifts.
For example, another V2 AI insurance client designed an AI solution that supports advisers in providing personalised product recommendations by interacting with existing Retire+ tools and systems to perform income calculations and rate look-ups, and suggest product features. It boosts adviser confidence and sales velocity by scaling and augmenting SME and BDM capabilities.
What Insurance Leaders Should Do Next
Rethink the entire customer experience, from onboarding and underwriting to claims and ongoing engagement.
Consider exploring the following use cases:
AI Use Case | What AI Does | Operational Impact |
AI-assisted Quotes and Claims case management | Automates and tracks quotes and claims processing workflows. | Reduced claims leakage Lower workload pressure Consistent claims progression |
Claims triage using computer vision | Reviews incident images to estimate severity, detect potential fraud, and route claims appropriately. | Faster handling of low-complexity claims Earlier specialist involvement for complex claims Improved customer experience |
AI-assisted underwriting for SME submissions | Extracts and structures data from submission packs, pre-filling underwriting systems, and flagging inconsistent information. | 30–50% reduction in manual prep time Faster quote turnaround Improved underwriting consistency |
AI-powered empathy in contact centres | Analyses customer tone, sentiment, and context in real time to guide agents with empathetic responses, suggested phrasing, and next best actions. | Improved customer satisfaction and trust More consistent service quality across the team Reduced escalations and complaints Lower onboarding time and cognitive load |
#2 AI is Redefining How Insurers Detect and Manage Risk
AI brings real-time intelligence to claims management, reducing insurance fraud while improving genuine customer service.
Historically, insurance has operated with partial and infrequent information about customers, assets, and behaviours. Infrequent interaction limits the provider’s ability to continuously monitor policyholders’ activities. This means insurance organisations often have to spend significant resources and face losses from fraudulent activity.
Recent Australian examples include fabricated claims supported by falsified documents, staged damage events, and other complex financial fraud. In other instances, the lack of clear, real-time insight has led to overly aggressive investigations, damaging customer trust, and exposing insurers to reputational risk.AI can change this dynamic.
AI enables continuous insight across the customer lifecycle. It can analyse patterns across claims histories, past information, documents, and behavioural signals to identify anomalies earlier and with greater precision.
Claims handlers are freed from manual analysis, allowing them to focus on complex cases and customer engagement.
For customers, this translates into clearer communication, more consistent decision-making, and faster resolution of legitimate claims.
At V2 AI, we are currently working on similar ideas for our customers.
What Insurance Leaders Should Do Next
Move from reactive detection to proactive risk identification, using AI to monitor, predict, and prevent loss, not just price it. Advantage will come when AI capability is paired with strong governance, transparency, and regulatory alignment.
Consider exploring the following use cases:
Use Case | What AI Does | Operational Impact |
Long-tail claim trajectory modelling | Predicts which claims are likely to deteriorate, remain open beyond expected duration, or experience cost escalation | Earlier clinical or legal intervention Improved reserving accuracy Reduced adverse development |
Litigation and legal cost analytics | Analyses legal correspondence, case histories, and jurisdiction patterns to predict litigation likelihood and flag cost blowouts early | Proactive settlement strategies, lower legal costs, improved claim outcome predictability |
#3 AI Is Transforming Regulatory Compliance for Insurance
Rather than viewing regulation as a constraint, insurers have an opportunity to use AI to turn compliance into a competitive advantage.
For decades, insurance compliance has been largely manual, document-heavy, and reactive. The operational burden of compliance continues to rise.
AI is beginning to change this dynamic. It can correlate large volumes of regulatory text with internal documentation and data to support continuous compliance. As AI adoption increases, compliance teams can shift from review to oversight, surfacing issues earlier at a far greater scale.
Having said that, it is important to note that, in Australia, regulators have not yet established clear certification frameworks for AI systems to perform compliance activities independently. As a result, human-in-the-loop models remain essential, with AI augmenting, not replacing, compliance decision-making.
Compliance is already emerging as a meaningful use case in financial services. It was a top-3 use case in the finance sector, according to the latest V2 State of AI report.
What Insurance Leaders Should Do Next
Consider exploring the following use cases within corporate risk management.
AI Use Case | How AI Supports Compliance | Example Compliance Outcomes |
AI-driven risk scanning for large corporate accounts | Continuously scans external data (news, filings, ESG disclosures, incident data) to identify emerging risks and document rationale for underwriting decisions | Stronger audit trails Defensible underwriting decisions Improved regulatory scrutiny outcomes |
AI-supported risk engineering | Analyses inspection reports and benchmarks risks across similar assets to generate standardised mitigation recommendations | Reduced decision variability, clearer evidence of consistent risk treatment |
#4 AI Is Reshaping How Insurance Creates Value
AI presents an opportunity to rethink the core insurance business model in ways not possible before.
Traditionally, insurance has operated through episodic, product-centric interactions. Customers engage when purchasing a policy, renewing coverage, or making a claim. However, AI enables a shift towards a continuous, relationship-driven business model, where insurers can connect with customers in new ways.
Some organisations are already moving in this direction. For example, an Australian-based insurance group offers a personalised, science-backed health and wellbeing program through an AI-powered app. Customers can get the support needed to make healthier lifestyle choices, and as their health improves, they enjoy premium discounts. The app is monetised through a healthcare and fitness vendor partner network.
What Insurance Leaders Should Do Next
Use AI to interact with customers more personally, leveraging it as a standout differentiator and to build brand loyalty. This could be more meaningful support through loss and hardship, automatically scaling products up or down as financial situations change or similar. The sky is the limit if AI and the customer are kept at the centre of the insurance model.
At V2, we are exploring some of the ideas below with our insurance customers.
Differentiation Lever | AI Use Case Examples |
Support through loss & hardship | Claims experiences that adapt tone and prioritise vulnerable customers Proactive reaching out to vulnerable customers - e.g., insured property owners in a disaster-affected area |
Proactive customer care | Timely nudges, such as payment support options, alerts, or coverage recommendations, before issues escalate |
Personalised communication | Clear, simple explanations of policies, claims decisions, and next steps, delivered in the right tone and channel at the right stage of the customer journey. |
Consistency across touchpoints | Maintains a unified view of the customer across digital, broker, and call centre interactions Customers receive the same context-aware experience regardless of channel or representative |
#5 AI is Reshaping Competition Across the Insurance Value Chain
From underwriting to claims, AI is compressing costs and accelerating innovation.
A recent CSIRO report explains how AI enhances customer outcomes amid insurance industry challenges like rising operational costs, natural disaster risks, and changing customer needs and expectations. It reviewed 624 AI use cases and found that 57% were launched within the past 2 years, with 61% planning further AI expansion.
AI is allowing insurers to quickly build products, marketing, customer service, and analytics that once required thousands of people. AI-driven automation of repeatable processes in claims processing, customer service, underwriting, and more can lower costs without impacting the customer experience. This is reducing cost to serve across the insurance value chain.
Incumbents are no longer at an advantage just because they have been in the market longer. AI is lowering barriers to entry and levelling the playing field.
What Insurance Leaders Should Do Next
Accelerate AI adoption to remain competitive and respond quickly to evolving customer needs. Complex frameworks, anti-discrimination guidelines, privacy and data protection laws, and AI-specific regulations can create uncertainty, especially given the limited AI-related exclusions and ambiguous policy wording, but risk awareness must not slow innovation.
Leaders should consider leveraging external help for sustainable innovation. Collaborating with strategic AI technology partners can help overcome these challenges without disrupting core operations.
For example, our V2 AI Velocity Framework with a product-led delivery approach enabled our insurance client to significantly shorten the innovation timeline. A key factor was our close collaboration with the client’s compliance and risk teams to define practical standards for evaluating and approving AI-driven solutions. We created a faster, repeatable pathway to bring AI capabilities into production.
Conclusion - AI as a Strategic Accelerator
In 2026, leading insurance organisations are reframing AI as a strategic accelerator rather than a technology upgrade. The organisations seeing results are:
Embedding AI into core insurance processing workflows (e.g., Quotes and Claims)
Reusing AI capabilities across different lines of business units, accelerating time-to-market
Modernisation of internal operations with AI enablement
Improvement of Empathy through measurement using AI
Centrally governing both internal and external AI use cases whilst keeping customer information safe.
They start top-down with clarity on value, such as revenue, productivity, resilience, or compliance. They then design operating models where AI is part of the control loop, not an isolated innovation experiment.
V2 AI has helped several insurance brands harness AI and maximise their competitive advantage. Our Enterprise Velocity Framework is a continuous delivery system that turns AI ambition into a practical, scalable advantage. We translate high-level business desires into technical reality within weeks, providing clear pathways to AI operations at scale. Contact us to learn more.
