This article, written by Thomas Rauschen, Vice-President and Global Industry Lead of Insurance at CGI, was originally published in Digital Insurance. Thomas shares his perspective on why insurers must move beyond simply adopting AI tools and instead focus on embedding AI strategically to unlock its full potential.
The insurance industry is fundamentally built on two concepts: risk management and transfer. While insurers provide customers risk and loss protection, they must also mitigate operational risks. New and emerging risks like climate change, demographic shifts, regulation and cybersecurity threats are reshaping the market, influencing how insurers manage business and risk strategies. They're also driving a powerful wave of innovation, with insurers turning to AI, automation and predictive analytics to protect customers, improve performance, and fuel growth.
AI and digitization are shaping the global insurance industry. Although insurers' maturity levels vary, AI already impacts critical processes like claims management, underwriting, and customer experience. But reaching a truly "risk-free," digital-first future is far from straightforward, and technology alone isn't enough to get there.
To realize AI's full potential, insurers must go beyond mere tool adoption. Implementing a strong "human-in-the-loop" framework to govern AI systems while minimizing operational, cyber, and data risks is crucial for success. This ensures responsible AI usage and resilience remain competitive.
The tangible benefits of AI in insurance
It's important to consider AI's tangible benefits and intangible components of a modernized insurance practice. Insurers are moving beyond a tactical approach toward comprehensive AI strategies across the value chain. In addition to tech investments, executives are considering cultural and behavioral changes to embed AI across the organization.
AI presents insurers with clear business cases during claims processing. Capabilities like text recognition, risk assessment and sentiment analysis enhance workflows — more dynamically and autonomously than traditional AI systems. Natural language processing (NLP) accelerates intake and reduces manual work by extracting and organizing data. Predictive analytics models can monitor and assess claim trends and severity, estimate repair costs, and flag fraudulent submissions. These tools improve accuracy and consistency, enabling employees to focus on complex, high-value cases that require human judgment.
AI transforms established underwriting, rating and pricing practices by moving toward more dynamic, data-driven understandings of customer segments and policyholders. It also supports the trend of personalized, usage- or behavior-based products and pricing. While actuarially sound and personalized premiums have long been an advantage, AI adds new depth by processing vast and varied data sources — from telematics and credit behavior to open data and lifestyle indicators. Through pattern recognition, AI and machine learning models find correlations between occupation, location, digital behavior and specific risk profiles that feed directly into underwriting and rating platforms, allowing premium calculations to adjust more precisely and instantly. The result is greater pricing accuracy and a more holistic, predictive view of individual risk, advancing both innovation and underwriting integrity.
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Another use case is fraud. AI enhances fraud detection and streamlines submission triage, automatically flagging high-risk submissions for deeper review by detecting anomalies and consolidating relevant information from internal and external sources. This allows underwriters to apply their expertise where it adds the most value, improving efficiency and accuracy.
Thinking beyond automation
AI presents compelling opportunities, but organizations need a clear view of their goals and objectives to develop a comprehensive AI strategy instead of focusing on tactical applications in both business and IT. The core business challenges and future capabilities needed to address them should drive technology architectures and enablement, not the other way around. Many strategies fall short because they focus on a single technology instead of exploring how different innovations intersect to create greater value.
One example is climate-related disasters, which 57% of insurance executives cite as a high-impact trend. While AI and automation can streamline processes and improve decision-making, managing climate risk requires an integrated, enterprise-wide approach. Insurers leverage earth observation data, predictive analytics, and long-range weather forecasting to anticipate and mitigate the impact of climate change. Yet technology alone isn't enough. True resilience depends on combining these insights with portfolio diversification, balancing high-risk exposures like coastal property or wildfire coverage with more sustainable products that help control loss ratios and maintain affordable premiums. It's this intersection of analytics, environmental intelligence, and disciplined underwriting that will define insurers capable of adapting to compounding risks.
Beyond technology: Building the human and strategic foundations
The human element is equally essential. Many insurers share a clear vision: digitizing core processes, enabling seamless interactions, and unlocking the value of interconnected data. Yet they often struggle with where to start, focusing on isolated initiatives or layering new tools onto rigid frameworks, fragmented data and legacy systems. Technologies used in isolation are band-aids that mask outdated systems instead of addressing root challenges. Successful digital transformation requires insurers to move beyond siloed technology fixes and create a comprehensive innovation roadmap balancing efficiency and cost with long-term investments in AI culture, change management, and organizational readiness. Aligning initiatives with clear purpose and measurable outcomes ensures innovation drives genuine progress, resilience and sustainable value.
A balanced approach
The next decade's winners will pair AI-driven efficiency with human insight, align digital initiatives with strategic business goals, and continuously recalibrate their risk strategies to an emerging risk landscape and global economy. By balancing innovation with intention and automation with authenticity, insurers can move beyond modernization to build a stronger, more resilient industry —protecting people, communities and economies for generations to come.
This article was originally published on December 29, 2025, in the online publication, Digital Insurance.
