The insurance industry stands at the threshold of a technological revolution that will fundamentally redefine how carriers assess risk, engage customers, and operate their businesses. Unlike previous waves of automation that merely digitized existing processes, 89% of insurance sector respondents plan to invest in GenAI in 2025, signaling a seismic shift toward artificial intelligence-driven transformation that promises to reshape every facet of the insurance value chain.

This transformation is powered by three revolutionary AI technologies: Generative AI, which can create personalized policies and dynamic pricing models; AI Agentics, which deploy autonomous agents capable of self-improving workflows from lead qualification to claims triage; and Large Action Models (LAMs), which execute complex multi-step processes like fraud detection and regulatory compliance with unprecedented speed and accuracy.

The financial stakes are enormous. Global Generative AI in Insurance Market was valued at USD 547.9 Million in 2023 and is poised to grow from USD 729.1 Million in 2024 to USD 8,064.95 Million by 2032, growing at a CAGR of 33.1%. More significantly, P&C insurers could reduce fraudulent claims and save between US$80 billion and US$160 billion by 2032 through AI-driven fraud detection alone. For carriers willing to embrace this transformation, AI represents not merely an operational enhancement but the foundation for building future-ready insurance organizations capable of competing with digital natives and meeting evolving customer expectations.

The AI Revolution in Insurance: Beyond Traditional Automation

Understanding the Generational Shift

The current wave of AI innovation represents a fundamental departure from traditional insurance technology approaches. While previous automation efforts focused on digitizing manual processes and improving operational efficiency, today’s AI technologies enable insurance companies to reimagine their entire business models. The distinction is critical: traditional automation made existing processes faster; AI makes entirely new approaches possible.

Early adopters using generative AI significantly in their customer-facing systems are seeing a marked improvement in customer satisfaction over insurers not using it at all, including a 14% higher retention rate and a 48% higher Net Promoter Score. This performance differential illustrates how AI transcends mere process improvement to deliver measurable business value that directly impacts competitive positioning.

The scope of transformation extends across all insurance functions. In 2030, underwriting as we know it today ceases to exist for most personal and small-business products across life and property and casualty insurance. The process of underwriting is reduced to a few seconds, as the majority of underwriting is automated. This prediction from McKinsey highlights the radical nature of the coming transformation—not incremental improvement, but fundamental reimagining of core insurance processes.

The Technology Foundation: Generative AI, AI Agentics, and Large Action Models

The three pillars of AI-powered insurance transformation each address different aspects of the industry’s operational challenges while collectively enabling comprehensive transformation.

Generative AI: This technology excels at creating content, analyzing unstructured data, and generating insights from complex datasets. In insurance applications, two-thirds (68%) of insurance professionals surveyed reported using some form of GenAI in their professional roles at least once a week. About 1 in 5 (22%) professed they use the technology daily. The technology’s ability to process vast amounts of unstructured data—from medical records and police reports to social media posts and IoT sensor data—enables insurers to make more informed decisions while reducing processing times from days to minutes.

AI Agentics: Agentic AI is an emerging technology that is set to transform industries everywhere. It combines new forms of artificial intelligence (AI) like large language models (LLMs), traditional AI such as machine learning, and enterprise automation to create autonomous AI agents. These systems move beyond simple automation to provide intelligent, self-improving workflows that can adapt to changing conditions without human intervention. In insurance, agentic AI can manage customer inquiries, process claims, and even negotiate settlements within predefined parameters.

Large Action Models (LAMs): Unlike large language models (LLMs) that focus on processing and generating text, LAMs are built to take concrete actions. For example, while an LLM might be used to understand and respond to customer inquiries, an LAM-based AI agent could autonomously handle the tasks those inquiries concern. LAMs represent the evolution from AI that thinks to AI that acts, enabling insurance companies to deploy systems that can independently execute complex, multi-step processes while maintaining oversight and control.

Transforming Core Insurance Functions

Underwriting Revolution: From Days to Seconds

The underwriting function exemplifies the transformative potential of AI technologies. Traditional underwriting relies heavily on human expertise to assess risk based on limited data points and standardized categories. AI-powered underwriting leverages vast datasets, real-time information feeds, and sophisticated modeling to deliver more accurate risk assessments in dramatically reduced timeframes.

AI technology can increase efficiency and automate workflows by accelerating underwriting processes, delegating tasks for human attention, offering better data-informed insurance policies faster, and improving customer experiences. With AI, underwriters can accurately pinpoint optimal rates based on the individual customer for optimal risk management. This capability enables insurers to move from broad risk categories to personalized pricing models that reflect individual risk profiles.

The data foundation for AI-driven underwriting extends far beyond traditional sources. These models are powered by internal data as well as a broad set of external data accessed through application programming interfaces and outside data and analytics providers. Information collected from devices provided by mainline carriers, reinsurers, product manufacturers, and product distributors is aggregated in a variety of data repositories and data streams. This comprehensive data integration enables underwriters to consider factors that were previously impossible to assess systematically.

Advanced AI applications in underwriting include predictive risk modeling that can anticipate future claim patterns, dynamic pricing that adjusts rates based on real-time risk factors, and automated decision-making for straightforward cases. AI in prefill solutions analyzes estimates to understand the intelligence they need and then mines a database to provide the appropriate data points for each field of commercial or residential property estimates. This automation reduces processing time while improving accuracy and consistency.

Claims Processing: Speed, Accuracy, and Customer Experience

Claims processing represents perhaps the most visible application of AI in insurance, where the technology’s impact on customer experience is immediately apparent. Traditional claims processing involves multiple manual steps, extensive documentation review, and coordination among various stakeholders. AI-powered claims processing can automate many of these steps while improving accuracy and reducing processing times.

For most simple claims, a fully automated end-to-end process redesign will embed AI across the entire claims journey, from data capture and triage to automated decision making and settlement. This kind of comprehensive transformation will enable real-time resolution for up to 70% of simple claims, cut operational costs by 30% to 50%, and significantly improve customer satisfaction. This level of automation represents a fundamental shift from reactive claims processing to proactive claims management.

The technology applications span multiple aspects of claims processing. Computer vision algorithms can assess damage from photographs and videos, natural language processing can extract relevant information from unstructured documents, and machine learning models can predict claim costs and identify potential complications. AI-powered machine learning can streamline claims processing. Notably, by analyzing images, sensors, and past data, AI tools allow insurers to quickly review claims and predict potential costs.

AI also enables more sophisticated fraud detection capabilities integrated directly into the claims process. Rather than conducting fraud investigations as separate activities, AI systems can continuously monitor claims for suspicious patterns and flag potential fraud in real-time. This approach reduces both processing costs and fraud losses while improving the experience for legitimate claimants.

Customer Engagement: Personalization at Scale

AI technologies enable insurance companies to deliver personalized customer experiences that were previously impossible at scale. Traditional insurance marketing and customer service relied on broad segmentation and standardized interactions. AI-powered customer engagement leverages individual customer data, behavioral patterns, and predictive analytics to deliver tailored experiences across all touchpoints.

Customers have different expectations of generative AI than what insurers anticipated. Generative AI is the tip of a well-known insurance iceberg: a complex technology estate that is aging and not always receptive to new gen AI models. This disconnect between customer expectations and insurer capabilities highlights the importance of strategic AI implementation that focuses on customer value rather than merely technological capability.

Personalization applications include dynamic pricing models that adjust rates based on individual risk factors and behaviors, targeted product recommendations that suggest relevant coverage options, and proactive customer service that anticipates needs based on life events or external factors. AI-powered chatbots and virtual assistants can handle routine inquiries while escalating complex issues to human agents with complete context and recommended solutions.

The integration of AI across customer touchpoints creates opportunities for continuous relationship enhancement. Rather than episodic interactions during policy purchases or claims events, AI enables ongoing engagement that adds value through risk prevention advice, policy optimization recommendations, and personalized educational content.

The Large Action Model Revolution

Understanding LAMs in Insurance Context

Large Action Models represent the next evolutionary step in AI capability, moving beyond analysis and recommendation to autonomous action execution. Just as LLMs made it possible to automate the generation of text and, in their multimodal forms, a wide range of media, LAMs may soon make it possible to automate entire processes. In insurance applications, this capability enables end-to-end process automation that was previously impossible.

The architecture of LAMs combines natural language understanding with action-taking capabilities through integration with external systems and APIs. AI agents often need to perform actions based on user input, such as retrieving information, scheduling tasks, or performing computations. Function calling allows the model to generate parameters for these tasks, enabling the agent to trigger external processes like database queries or API calls. This architecture enables LAMs to execute complex insurance workflows autonomously while maintaining appropriate oversight and control mechanisms.

In practical insurance applications, LAMs can handle multi-step processes such as policy application processing, where the system evaluates the application, orders external reports, assesses risk factors, determines pricing, and issues a policy quote—all without human intervention. Similarly, in claims processing, LAMs can receive first notice of loss, order necessary reports, schedule inspections, evaluate coverage, and authorize payments for straightforward claims.

Agentic AI: Self-Improving Workflows

Agentic AI systems bring autonomous decision-making and continuous learning capabilities to insurance operations. Agentic AI uses self-contained agents — AI models that autonomously complete tasks and manage workflows using machine learning, algorithms, and predictive analytics to make real-time decisions. These systems can adapt to changing conditions, learn from experience, and optimize their performance over time.

The five-step process of agentic AI—perceive, plan, reason, act, and learn—enables sophisticated automation across insurance functions. Large language model (LLMs) guides the reasoning process — understanding tasks, crafting solutions, and coordinating specialized models for jobs like content generation or image analysis. This reasoning capability allows agentic systems to handle complex situations that require contextual understanding and nuanced decision-making.

In underwriting applications, agentic AI can continuously monitor risk factors, adjust pricing models based on emerging trends, and identify new data sources that improve risk assessment accuracy. For customer service, agentic systems can learn from customer interactions to improve response quality and identify opportunities for proactive outreach. The self-improving nature of these systems means that their value increases over time as they accumulate experience and refine their capabilities.

Implementation Considerations and Challenges

The deployment of LAMs and agentic AI in insurance requires careful consideration of governance, oversight, and integration challenges. Of course, if trust is already a challenge when it comes to generating text and images — and it certainly is — it’s an even bigger one when it comes to taking action. Insurance companies must establish robust governance frameworks that define appropriate use cases, implement necessary safeguards, and maintain human oversight for critical decisions.

Integration with existing systems presents both technical and organizational challenges. Legacy insurance systems were not designed to support autonomous AI agents, requiring significant infrastructure investment and system redesign. Additionally, regulatory compliance requirements add complexity to LAM deployment, as these systems must operate within established guidelines while maintaining auditability and explainability.

The human factor remains critical even in highly automated environments. While LAMs can execute many processes autonomously, human expertise continues to be essential for complex cases, exception handling, and strategic decision-making. The most successful implementations will likely involve hybrid approaches that leverage AI capabilities while preserving human judgment for appropriate situations.

Fraud Detection: AI as the Ultimate Defense

The Scale of the Fraud Challenge

Insurance fraud represents one of the most significant financial drains on the industry, with estimates suggesting losses in the hundreds of billions annually. According to the Coalition Against Insurance Fraud, insurance fraud costs the insurance industry more than $308.6 billion per year. Traditional fraud detection methods, which rely primarily on rules-based systems and manual investigation, struggle to keep pace with the sophistication and scale of modern fraud schemes.

The evolution of fraud patterns has outpaced traditional detection capabilities. Modern fraudsters employ sophisticated techniques, including staged accidents, organized fraud rings, identity theft, and application fraud that often escape detection by conventional methods. Actions like exaggerated claims or forms containing minor inconsistencies are brutal for underwriters to detect without technology to assist them. This “soft fraud” represents a particularly challenging detection problem that requires advanced analytical capabilities.

The emergence of AI-assisted fraud adds another layer of complexity. Because they are trained on large language models, generative AI tools like ChatGPT are skilled at mimicking human speech and writing. Users feel like they are talking to real people, but AI-based chatbots also make all-too-human mistakes. Fraudsters are increasingly using AI tools to create convincing fake documents, medical records, and claims narratives that can fool traditional detection systems.

AI-Powered Fraud Detection Capabilities

Artificial intelligence transforms fraud detection from a reactive, investigation-based approach to a proactive, predictive capability that can identify potential fraud in real-time. Insurers that integrate multimodal capabilities using AI and advanced analytics could generate potential savings of 20% to 40%, depending on the implementation, type of insurance, and sophistication of fraud detection systems. These capabilities extend far beyond traditional rule-based systems to encompass sophisticated pattern recognition and behavioral analysis.

The technical capabilities of AI fraud detection systems include analysis of vast volumes of structured and unstructured data from multiple sources. AI can analyze vast volumes of structured and unstructured data from various sources, such as claim forms, medical records, photos, voice recordings, etc. Technologies like optical character recognition (OCR), natural language processing (NLP), and voice analysis allow insurance companies to extract meaningful insights from these data types. This comprehensive data analysis enables the detection of subtle patterns and anomalies that would be impossible for human investigators to identify systematically.

Machine learning algorithms continuously improve their detection capabilities by learning from historical fraud cases and identifying new patterns. AI algorithms identify relationships within historical data, creating statistical norms. They constantly monitor incoming claims, flagging any outlying details. This adaptive capability means that AI systems become more effective over time as they encounter new fraud schemes and refine their detection criteria.

Real-Time Detection and Prevention

One of the most significant advantages of AI-powered fraud detection is the ability to identify suspicious activity in real-time, enabling prevention rather than investigation after the fact. The capacity to analyze data fast and in real-time is one of the biggest USPs of insurance fraud detection utilizing machine learning and artificial intelligence. As a result, insurance companies invest more time avoiding fraud than recovering from it. This shift from reactive to proactive fraud management represents a fundamental improvement in fraud control effectiveness.

Real-time fraud detection capabilities include automated risk scoring for all claims, continuous monitoring of claimant and provider behavior patterns, and immediate flagging of suspicious activities for human review. The routines and behaviors of claimants and policyholders are continuously monitored by AI systems. The algorithms can instantly spot potentially fraudulent behavior and immediately notify the business when a claim requires further examination. This immediate response capability enables insurers to take preventive action before fraudulent payments are processed.

The integration of multiple data sources and analytical techniques enhances detection accuracy while reducing false positives. Advanced AI systems can correlate information from social media, public records, telematics data, and other sources to build comprehensive risk profiles. This holistic approach improves detection accuracy while minimizing the disruption to legitimate claimants that results from false fraud allegations.

Financial Impact and ROI

The financial benefits of AI-powered fraud detection extend beyond simple cost savings to include improved operational efficiency and enhanced customer experience. Deloitte predicts that, by implementing AI-driven technologies across the claims life cycle and integrating real-time analysis from multiple modalities, P&C insurers could reduce fraudulent claims and save between US$80 billion and US$160 billion by 2032. These savings result from both direct fraud prevention and operational efficiency improvements.

The ROI calculation for AI fraud detection systems must consider multiple benefit categories. Return on Investment (ROI) in AI-based fraud detection can be measured through various metrics, such as cost savings from reduced fraud losses and increased efficiency in claims processing. Direct benefits include reduced fraudulent payouts, lower investigation costs, and faster processing of legitimate claims. Indirect benefits include improved customer satisfaction, enhanced brand reputation, and reduced regulatory scrutiny.

The speed advantage of AI systems contributes significantly to cost savings. AI can process massive datasets much faster than humans by connecting complex patterns. Even small anomalies that indicate fraud can be identified within minutes rather than days or weeks. This rapid processing capability enables insurers to make more informed decisions quickly while reducing the administrative costs associated with prolonged investigations.

Digital Distribution and Customer Acquisition

AI-Enhanced Distribution Strategies

The transformation of insurance distribution through AI technologies enables carriers to reach customers through new channels while improving the effectiveness of existing distribution methods. For direct writers, autonomous sales agents will play a key role, particularly in the upper stages of the sales funnel. These AI-driven agents can efficiently process large volumes of unqualified leads, directing customers to the most suitable sales journey. This capability enables more efficient resource allocation and improved conversion rates across distribution channels.

AI-powered distribution strategies leverage predictive analytics to identify prospects most likely to purchase coverage, personalize marketing messages based on individual preferences and life events, and optimize pricing strategies for different customer segments. The technology enables dynamic pricing models that adjust rates based on real-time risk factors and competitive positioning, allowing carriers to remain competitive while maintaining profitability.

The integration of AI across multiple distribution channels creates a seamless customer experience that spans digital platforms, agent interactions, and direct marketing efforts. Customers can begin their insurance journey on one channel and seamlessly transition to another without losing context or repeating information. This omnichannel approach, powered by AI, improves customer satisfaction while reducing acquisition costs.

Embedded Insurance and Ecosystem Integration

Embedded insurance represents one of the most promising applications of AI technology in distribution. While AI monetization lags, embedded insurance is set to grow by 30%, especially in personal lines. In Forrester’s Priorities Survey, 2024, 32% of global business and technology professionals at insurance firms said they plan to invest more in embedded finance capabilities in 2025. This growth reflects the potential for AI to enable seamless insurance integration into non-insurance products and services.

AI technologies enable the sophisticated risk assessment and pricing capabilities required for embedded insurance products. Real-time data analysis, predictive modeling, and automated underwriting make it possible to offer insurance coverage at the point of sale for products ranging from electronics and automobiles to travel and events. The speed and accuracy of AI-powered underwriting enable carriers to provide instant coverage decisions without human intervention.

The ecosystem integration capabilities of AI extend beyond simple embedded products to encompass comprehensive risk management partnerships. AI systems can monitor usage patterns, environmental factors, and behavioral indicators to provide dynamic coverage that adjusts based on actual risk exposure. This capability enables new insurance models such as usage-based coverage and on-demand protection that align coverage with actual risk.

Customer Experience Transformation

AI technologies enable insurance companies to deliver customer experiences that rival those provided by leading digital companies across all industries. The experience of purchasing insurance is faster, with less active involvement on the part of the insurer and the customer. Enough information is known about individual behavior, with AI algorithms creating risk profiles, so that cycle times for completing the purchase of an auto, commercial, or life policy will be reduced to minutes or even seconds. This dramatic improvement in the purchase experience removes traditional barriers to insurance adoption.

Personalization capabilities extend throughout the customer lifecycle, from initial marketing contact through policy servicing and claims resolution. AI systems can anticipate customer needs based on life events, environmental factors, and behavioral patterns, enabling proactive outreach with relevant product recommendations and risk management advice. This predictive capability transforms the insurance relationship from reactive coverage to proactive risk partnership.

The integration of AI-powered customer service capabilities creates consistent, high-quality interactions across all touchpoints. Intelligent chatbots can handle routine inquiries while seamlessly escalating complex issues to human agents with complete context and recommended solutions. This hybrid approach ensures that customers receive appropriate assistance while optimizing resource allocation and reducing service costs.

Implementation Challenges and Strategic Considerations

Technology Infrastructure and Integration

The successful implementation of AI technologies in insurance requires significant investment in technology infrastructure and careful integration with existing systems. Technical debt in insurance core systems makes it difficult to adapt these systems to new AI capabilities amid quickly changing market conditions, customer preferences, and regulatory requirements. Legacy systems present particular challenges for AI integration, as they were not designed to support the data flows and processing requirements of modern AI applications.

Infrastructure considerations include cloud computing capabilities to support AI workloads, data management systems that can handle vast volumes of structured and unstructured data, and integration platforms that connect AI systems with existing insurance applications. The complexity of these requirements often necessitates significant system redesign rather than simple technology overlay.

Data quality emerges as a critical factor in AI implementation success. 52% of executives cite data constraints—inadequate, inaccessible, incomplete, or otherwise unusable data—as slowing the speed to market of products. Insurance companies must invest in data governance capabilities, data cleansing processes, and data integration technologies to ensure that AI systems have access to high-quality, comprehensive information.

Regulatory Compliance and Risk Management

The deployment of AI technologies in insurance operates within a heavily regulated environment that requires careful attention to compliance requirements and risk management considerations. In 2024, the Third-Party Data and Models (H) Task Force was formed to evaluate and develop a regulatory framework around the use of third-party AI data and models by insurance companies. Regulatory frameworks continue to evolve as regulators grapple with the implications of AI technology for consumer protection and market stability.

Key compliance considerations include algorithmic transparency and explainability requirements, data privacy and protection regulations, fair lending and anti-discrimination provisions, and consumer disclosure obligations. AI systems must be designed to provide adequate explanations for their decisions, particularly in areas such as underwriting and claims, where those decisions directly impact consumers.

Risk management frameworks must address the unique challenges posed by AI systems, including model risk, data security vulnerabilities, operational risk from system failures, and reputational risk from algorithmic bias or errors. Insurers should monitor the latest AI-assisted hoaxes and learn more about how to harness the power of AI to uncover potential fraud throughout the insurance pipeline. This monitoring requirement extends to both defensive applications against AI-assisted fraud and proactive management of AI system risks.

Talent and Organizational Change

The transformation to AI-powered insurance operations requires significant investment in talent development and organizational change management. Our survey findings uncover gaps in preparedness, but a strategic focus on resources, responsibility, and returns could help insurers up their POC to production game. Many insurance companies struggle to move from AI proof-of-concept projects to full-scale production implementations due to talent and organizational constraints.

Required capabilities include data science and analytics expertise, AI system design and implementation skills, change management capabilities for business process transformation, and governance and risk management competencies specific to AI applications. The shortage of AI talent in the market requires insurance companies to develop comprehensive talent strategies that combine hiring, training, and partnership approaches.

Organizational change considerations encompass cultural adaptation to AI-augmented workflows, redefinition of job roles and responsibilities, development of new performance metrics and incentive systems, and establishment of governance structures for AI system oversight. Success requires coordination across multiple organizational functions and sustained leadership commitment to transformation objectives.

Scaling and Production Deployment

Moving from AI pilot projects to enterprise-scale production deployments presents significant challenges that many insurance companies struggle to navigate successfully. Many insurers find themselves stuck in pilot purgatory, with promising tests that never scale to production. According to McKinsey’s 2024 report on generative AI in corporate functions, the reality is far more nuanced than the hype suggests. This scaling challenge reflects the complexity of enterprise AI deployment rather than any fundamental limitation of the technology.

Scaling considerations include system performance and reliability requirements under production loads, integration complexity with multiple existing systems and processes, governance and oversight mechanisms for production AI systems, and change management for widespread organizational adoption. Each of these factors requires careful planning and substantial resource investment.

The economics of AI deployment also change significantly at scale. While pilot projects may show promising results with limited data and simple use cases, production systems must handle the full complexity and variability of insurance operations. This transition often requires additional investment in infrastructure, data management, and system integration that was not apparent during initial pilot phases.

Future Trends and Emerging Opportunities

The Evolution of AI Capabilities

The rapid pace of AI development ensures that current capabilities represent only the beginning of the transformation potential in insurance. The barriers to scaling GenAI remain challenging as organizations take steps to break through. Companies start “getting real” by prioritizing the serious work of AI scaling and value creation. As organizations move beyond initial experimentation to serious implementation, new capabilities and applications will emerge.

Emerging AI capabilities include multimodal AI systems that can process and analyze text, images, audio, and video simultaneously, enabling more comprehensive risk assessment and claims processing. Advances in edge computing will enable real-time AI processing on mobile devices and IoT sensors, opening new possibilities for usage-based insurance and real-time risk monitoring.

The development of more sophisticated reasoning capabilities will enable AI systems to handle increasingly complex insurance scenarios that currently require human expertise. These advances will likely extend AI applications to complex commercial insurance, specialty lines, and regulatory compliance functions that are currently beyond the scope of AI automation.

Industry Ecosystem Transformation

AI technologies are driving fundamental changes in the insurance industry ecosystem, creating new partnership opportunities and competitive dynamics. Insurers will continue to invest in AI for internal automation, but the true potential of AI, such as improving underwriting or claims accuracy, will remain elusive for most. This prediction suggests that AI adoption will create competitive advantages for early adopters while challenging traditional industry structures.

Ecosystem changes include the emergence of AI-native insurtech companies that compete directly with traditional carriers, new partnership models between insurers and technology companies, increased integration between insurance and non-insurance companies through embedded products, and the development of industry-wide AI infrastructure and data sharing initiatives.

The transformation of distribution channels through AI will likely accelerate the shift toward digital-first insurance models while creating new opportunities for value-added services and risk prevention. Traditional agency and broker models will need to adapt by incorporating AI capabilities to remain competitive with direct digital channels.

Regulatory and Ethical Considerations

The continued evolution of AI in insurance will likely prompt further regulatory development and increased focus on ethical AI implementation. Following analysis of this data, the Working Group is considering the need for clarification of, or additional, insurance laws and regulations. Regulatory frameworks will need to balance innovation promotion with consumer protection and market stability concerns.

Key areas of regulatory focus will likely include algorithmic transparency and explainability requirements, fairness and anti-discrimination provisions, data privacy and security standards, and consumer protection measures for AI-driven products and services. Insurance companies must anticipate these regulatory developments and build compliance capabilities into their AI systems from the beginning.

Ethical considerations extend beyond regulatory compliance to encompass broader questions about the appropriate role of AI in insurance decision-making. Issues such as the digital divide, algorithmic bias, and the balance between efficiency and human judgment will require ongoing attention as AI capabilities expand.

Strategic Recommendations for Insurance Executives

Developing AI Strategy and Governance

Insurance executives must approach AI transformation with a comprehensive strategy that aligns technology capabilities with business objectives while addressing risk management and regulatory requirements. Our 2024 global Build for the Future survey, a continuation of our studies into digital transformation and AI maturity, shows that pacesetters expect to extract twice as much value as laggards by focusing their AI investments. This finding emphasizes the importance of strategic focus rather than broad-based experimentation.

Strategic considerations include identifying high-impact use cases that align with business priorities, developing a roadmap for phased AI implementation that builds capabilities progressively, establishing governance frameworks that ensure appropriate oversight and risk management, and creating partnerships with technology providers and other insurers to accelerate development.

Governance frameworks must address data management and quality standards, model development and validation processes, system monitoring and performance management, and regulatory compliance and ethical considerations. These frameworks should be designed to support innovation while ensuring that AI systems operate safely and effectively.

Investment Priorities and Resource Allocation

Among the industry’s goals for investing in GenAI, the top three emerged as: Improvement in customer satisfaction and retention (81%, the highest of any industry segment). Reduction in operational costs and time savings (76%). Enhanced risk management and compliance measures (72%). These priorities provide a framework for evaluating AI investment opportunities and measuring success.

Investment considerations include technology infrastructure that can support AI applications at scale, talent development programs that build internal AI capabilities, data management capabilities that ensure high-quality inputs for AI systems, and partnerships with technology providers that can accelerate development and reduce risk.

Resource allocation decisions should consider both short-term efficiency gains and long-term competitive advantages. While AI can deliver immediate benefits in areas such as fraud detection and customer service, the greatest value may come from transformational applications that reshape business models and create new market opportunities.

Building Competitive Advantage Through AI

The successful implementation of AI technologies can create sustainable competitive advantages for insurance companies willing to invest in comprehensive transformation. 77% of industry executives said they need to adopt generative AI quickly to keep up with rivals. This urgency reflects the potential for AI to create significant competitive differentiation in insurance markets.

Competitive advantage sources include superior risk assessment capabilities that enable better pricing and selection, enhanced customer experiences that improve acquisition and retention, operational efficiency that reduces costs and improves margins, and new product capabilities that address previously unmet market needs.

Building a competitive advantage requires more than technology implementation; it demands organizational transformation that embeds AI capabilities throughout the business. This transformation includes developing AI-native business processes, creating data-driven decision-making cultures, and establishing continuous learning and improvement capabilities that enable ongoing AI advancement.

Embracing the AI-Powered Future

The insurance industry stands at a defining moment where the convergence of Generative AI, AI Agentics, and Large Action Models creates unprecedented opportunities for transformation and competitive advantage. The global AI in insurance market size was $4.59 billion in 2022 and is projected to reach approximately $79.86 billion by 2032, growing at a compound annual growth rate (CAGR) of 33.06% from 2023 to 2032. This explosive growth reflects not merely technological enthusiasm but fundamental recognition that AI capabilities have matured to the point where they can deliver measurable business value across all insurance functions.

The evidence of AI’s transformative potential is overwhelming. From fraud detection systems that could save between US$80 billion and US$160 billion by 2032 to underwriting processes that reduce cycle times from days to seconds, AI technologies enable improvements in both efficiency and effectiveness that were previously impossible. The companies that successfully harness these capabilities will not merely optimize existing operations—they will fundamentally reimagine what insurance can be.

However, the path to AI transformation is not without challenges. Technical infrastructure requirements, regulatory considerations, talent constraints, and organizational change management all require sustained attention and investment. A strategic focus on resources, responsibility, and returns could help insurers up their POC to production game. Success demands more than technology deployment; it requires comprehensive organizational transformation that embeds AI capabilities throughout the business.

The competitive implications of AI adoption extend far beyond individual company performance to reshape entire industry dynamics. Early adopters gain significant advantages in risk assessment accuracy, operational efficiency, and customer experience quality. As these advantages compound over time, they create competitive moats that become increasingly difficult for laggards to overcome.

The insurance companies that thrive in this AI-powered future will be those that approach transformation strategically, focusing on high-impact use cases while building comprehensive capabilities across technology, talent, and organizational change. They will embrace the reality that AI is not merely a tool to improve existing processes but a foundational technology that enables entirely new business models and customer relationships.

For financial services executives, the message is clear: the AI revolution in insurance has moved beyond experimentation to implementation. The question is no longer whether to adopt AI technologies but how quickly and effectively to do so. Organizations that act decisively to build AI capabilities today will position themselves as industry leaders tomorrow, while those that delay risk becoming irrelevant in an increasingly AI-driven marketplace.

The future of insurance is being written now, powered by artificial intelligence technologies that promise to make the industry more efficient, more accurate, and more responsive to customer needs than ever before. The companies that seize this opportunity will not only survive the transformation—they will lead it, creating new standards for what insurance can and should be in the digital age.