AI Is Changing the Way Insurance Delivers Value

With AI adoption accelerating across property and casualty (P&C) and life and annuities (L&A), insurers are moving beyond automation into true intelligence, where risk is anticipated, not just managed.

As insurers mature in their data capabilities, they unlock the full potential of AI, shifting from reacting to risks to predicting and preventing them. For example, AI enables carriers to forecast claim frequency and severity, identify high-risk behaviors before incidents occur, and personalize coverage and pricing based on predictive insights. 

But realizing this potential is where insurers begin to diverge: those with advanced AI maturity are pulling ahead, while others remain stuck in early stages, struggling to scale beyond experimentation.

Why some insurers are pulling ahead

According to the 2025 Evident AI Insurance Index, a handful of carriers, like AXA and Allianz, are significantly ahead in AI maturity, thanks to years of strategic investment. These leaders are embedding AI across underwriting, claims and customer engagement. Early data suggests a correlation between higher AI maturity and stronger financial performance, including improved loss ratios. 

Most established insurers have already launched AI initiatives, even before the rise of generative AI, signaling long-term commitment. What sets the front-runners apart is their focus on talent development and innovation through research, partnerships and venture activity.

Why others are falling behind

While leading insurers are making significant strides with AI, many others continue to face persistent challenges. Talent shortages, inconsistent governance and fragmented data environments are common barriers that slow progress. In fact, according to Equisoft, 78 percent of L&A insurers cite unstructured, siloed data as the biggest obstacle to realizing AI's full value. 

Responsible AI is another critical concern across the industry. Carriers are increasingly focused on managing bias, improving data quality, and meeting regulatory requirements for traceability and auditability. Many are cautious about introducing new risks through AI, especially when it comes to customer trust and compliance. European carriers tend to lead in this area, driven by stricter regulatory pressure and more mature governance frameworks.

Early wins and use cases

Insurance is still in the early to middle stages of AI adoption compared to banking and other sectors. However, insurers that made early investments in AI are already seeing a measurable impact across core functions, as illustrated above. 

From streamlining operations to enhancing customer touchpoints, here are some of the most promising use cases driving value today:

• Claims processing: AI tools like computer vision and natural language processing (NLP) streamline claims intake, damage assessment and settlement, reducing cycle times and improving customer satisfaction.
• Underwriting: Agentic AI systems automate risk evaluation, ingesting structured and unstructured data to generate highly accurate recommendations at scale.
• Fraud detection: AI models trained on behavioral and anomaly detection patterns outperform traditional rule-based systems, flagging suspicious claims earlier and reducing losses.
• Customer experience: Intelligent digital assistants deliver instant, personalized support, while AI agents proactively dispatch assistance based on sensor data and policy details.

Whether your organization is just beginning to explore AI or has already started to see early benefits, the next step is to consider how these efforts can be developed into a sustainable, scalable strategy for long-term impact.

Chart your next steps with WWT's AI Maturity Model

Achieving AI maturity is a journey, not a destination. WWT's AI Maturity Model provides insurers with a clear framework to assess their current state, benchmark progress against industry peers and regulatory expectations, and chart a path toward scalable, responsible AI adoption.

WWT's AI Maturity Model

Here's a closer look at how WWT's AI Maturity Model applies within the insurance landscape.
 

Model overview

Level 1: Exploratory - AI interest is scattered, with siloed efforts and no strategic direction.
Level 2: Experimental - Individual teams begin exploring AI use cases, often without alignment or dedicated resources.
Level 3: Operational - AI is prioritized and governed centrally, with formal processes, tools and dedicated teams in place.
Level 4: Transformational - AI is embedded across the organization, driving strategic value, continuous improvement and enterprise-wide integration.

Examples by level

* L1 ➜ L2: Run isolated pilots for claims automation using NLP; experiment with AI-powered chatbots for FAQs; test simple fraud flagging rules augmented with machine learning; assess data quality and readiness to prepare for scaled AI projects

* L2 ➜ L3: Prototype computer vision models for automated damage assessment; leverage generative AI for claims processing and communications; develop centralized AI governance councils or sandboxes for safe experimentation; validate risk scoring models in a controlled lab environment

* L3 ➜ L4: Deploy AI-driven fraud detection in production claims workflows; scale AI agents into workflows; standardize secure data pipelines across lines of business; integrate AI into underwriting for real-time risk evaluation; establish enterprise-wide governance frameworks and continuous monitoring for proactive model management, AIOps integration and compliance

Is your organization ready to move up the AI maturity curve?