From “Using AI” to Being AI-Native: How Insurers Actually Change the Operating Model
Artificial intelligence is reshaping industries, and insurance is no exception. Many insurers are experimenting with AI, launching pilot projects and purchasing sophisticated software. Yet, a fundamental gap often remains between buying AI tools and truly becoming an AI-native organization. The real transformation lies not in isolated projects, but in weaving AI into the very fabric of the company - its operating model, its talent, and its core processes. This shift requires moving beyond proofs-of-concept to a deliberate, structural evolution.
This article explores what it takes to build a truly AI-native insurer. We will cover the shift from scattered projects to a cohesive AI operating model, identify key areas where AI delivers financial impact, and discuss how to redesign work to blend human expertise with machine intelligence. We will also outline a roadmap for making this transition a reality.
From AI Projects to an AI Operating Model
The initial enthusiasm for AI often leads insurers down a path of fragmented experimentation. A team in claims might test a fraud detection algorithm, while another in underwriting pilots a new risk assessment tool. While these projects can yield localized benefits, they fail to create lasting, enterprise-wide change. The problem is that they treat AI as a feature to be added, not a foundation upon which to build.
An AI-native insurer, by contrast, operates on a completely different premise. It builds its entire business around a core of data and algorithms, creating a unified system where insights flow freely across departments. This isn't just about technology; it's about creating a new operating model.
This model is characterised by:
Centralised Data Infrastructure: Data is treated as a shared asset, accessible across the organisation. Silos are broken down to create a single source of truth that feeds all AI systems.
Integrated AI Capabilities: Instead of standalone tools, AI capabilities are built as connected services that can be used by different parts of the business. An insight from a claims process could, for example, instantly inform underwriting guidelines.
Continuous Learning Loops: The system is designed to learn and improve constantly. Every customer interaction, every claim processed, and every policy underwritten generates new data that refines the underlying algorithms, creating a powerful competitive advantage that grows over time.
Where AI Impacts the P&L
To justify the significant investment required, leaders must focus AI initiatives on areas that directly affect the profit and loss (P&L) statement. The impact of AI is most profound in four key domains:
1. Claims Processing
The claims function is ripe for AI-driven transformation. AI can automate damage assessment from images, predict claim severity, and detect fraudulent activity with greater accuracy than ever before. By automating routine tasks, straight-through processing rates can increase dramatically, reducing operational costs while accelerating settlement times automating routine tasks to improve efficiency and customer experience and optimizing claims workflows. This not only improves efficiency but also leads to a better customer experience during a critical moment of truth.
2. Underwriting and Pricing
Traditional underwriting relies heavily on historical data and manual analysis. AI revolutionises this by enabling underwriters to analyse vast and diverse datasets - from telematics and IoT sensors to satellite imagery and social media trends. This allows for more granular, accurate, and dynamic risk pricing. Machine learning models can identify subtle patterns that human analysts might miss, leading to a more profitable and balanced risk portfolio.
3. Customer Experience
AI-powered chatbots and virtual assistants can provide customers with 24/7 support, answering queries and guiding them through simple processes like obtaining a quote or filing a first notice of loss. Personalisation engines can analyse customer data to recommend the right products at the right time, increasing cross-sell and upsell opportunities. This creates a seamless, responsive, and highly personalised experience that builds loyalty.
4. Operations
Beyond customer-facing functions, AI can streamline back-office operations. It can automate document processing, optimise workforce scheduling, and predict operational bottlenecks before they occur. By reducing manual effort and improving efficiency, AI frees up employees to focus on higher-value activities that require human judgment and creativity.
Redesigning Work: The Human-Machine Partnership
A common fear is that AI will replace human jobs. In an AI-native organisation, however, the goal is not replacement but augmentation. The most successful insurers will be those that master the art of blending machine intelligence with human expertise. This requires a fundamental redesign of work.
Decisions can be categorised into three types:
Fully Automated Decisions: High-frequency, low-stakes decisions, such as processing a simple windscreen claim, can be fully automated. The machine handles the entire process, freeing up human capacity.
Machine-Assisted Decisions: For more complex scenarios, like underwriting a non-standard commercial property, AI can act as a co-pilot. The system analyses the data, flags key risks, and presents a recommendation, but the final decision rests with the experienced human underwriter.
Human-Led Decisions: The most complex, strategic, and relationship-driven decisions remain firmly in the human domain. This includes handling a highly sensitive and complex claim or negotiating a large corporate policy. Here, technology provides support, but intuition, empathy, and strategic thinking are paramount.
Building the Right Talent and Governance
Technology alone is not enough. An AI-native insurer needs a new kind of talent stack and a governance structure that fosters and rewards AI-driven innovation.
The ideal team brings together three key roles:
Domain Experts (e.g., Underwriters, Claims Adjusters): These are the people with deep industry knowledge. They understand the nuances of risk and the realities of the business.
Data Scientists and Engineers: These are the technical experts who build and maintain the AI models and data infrastructure.
AI Translators: This is a crucial bridging role. Translators are individuals who are fluent in both the language of business and the language of data science. They help define business problems in a way that data scientists can solve and explain the outputs of complex models in a way that business leaders can understand and act upon.
This talent must be supported by a robust governance framework. Key Performance Indicators (KPIs) should be redefined to measure the impact of AI, such as the percentage of claims processed automatically or the uplift in profitability from AI-driven pricing. Incentives should be aligned to encourage cross-functional collaboration and reward teams for achieving AI-related outcomes, not just for completing projects.
A Roadmap for Transformation: From POC to Structural Change
Transitioning from an experimental phase to an AI-native operating model is a multi-year journey. Here is a potential 12-to-24-month roadmap to guide the process.
Months 1–6: Foundation and Strategy
Establish a Vision: Secure executive buy-in and define what "AI-native" means for your organisation.
Identify Priority Use Cases: Focus on 2–3 high-impact areas where AI can deliver clear value (e.g., personal auto claims, small business underwriting).
Form a Cross-Functional Team: Assemble your initial team of domain experts, data scientists, and an AI translator.
Assess Data Readiness: Conduct an audit of your data infrastructure and create a plan to address gaps.
Months 7–12: Pilot and Learn
Develop a Minimum Viable Product (MVP): Build and launch your first AI model in a controlled environment. For example, have an AI model shadow human underwriters to compare its recommendations.
Redesign the Workflow: Map out how the new AI tool will integrate into the existing process and how human roles will change.
Measure and Refine: Track the performance of the MVP against predefined KPIs. Gather feedback from users and continuously refine the model and the workflow.
Months 13–24: Scale and Structural Change
Scale Successful Pilots: Once a pilot has proven its value, develop a plan to roll it out across the relevant business unit.
Build the "AI Factory": Begin building the centralized data platforms and reusable AI components that will form your core AI operating model. This "factory" approach allows you to develop and deploy new AI solutions more quickly and efficiently.
Invest in Talent and Training: Launch upskilling programmes to develop AI literacy across the organization. Actively recruit for key roles like AI translators.
Evolve Governance: Embed AI-related KPIs and incentives into performance management systems. Establish an AI ethics committee to oversee the responsible use of algorithms.
Becoming an AI-native insurer is not a simple upgrade - it is a profound organisational transformation. It demands more than just investment in technology. It requires a clear vision, a focus on tangible business value, a commitment to redesigning work, and a culture that embraces the powerful partnership between human and machine intelligence. The insurers that successfully navigate this journey will not just be more efficient; they will be smarter, faster, and better equipped to thrive in the future of risk.
