Why insurers’ AI ambitions are exposing the cloud execution gap

AI makes it possible for insurance claims adjusters to instantly access every relevant policy detail. Fraud analysts can spot suspicious patterns in real time, and customers can receive personalized answers to complex questions about their cover in seconds rather than days.

To get these results, insurers are investing heavily in AI — only to discover that the biggest obstacle to success is not the technology itself but the foundation underneath it.

This is because AI models depend on something many insurers are still working to modernize: cloud infrastructure and data platforms. When these foundations aren’t in place, even the most ambitious AI initiatives can struggle to move beyond isolated use cases.

Research from our global report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, shows that cloud modernization is the number-one priority for insurers over the next two years. Nearly all insurance respondents (98%) say the rise of AI and agentic AI has increased the need for cloud investment, with 95% acknowledging that their current cloud investment levels put AI, cloud-native and modernization initiatives at risk.

Can they modernize fast enough to operationalize AI securely and efficiently at enterprise scale?

AI is changing what insurers need from the cloud

For years, cloud transformation programs in insurance were associated mainly with infrastructure modernization, cost optimization and migration from legacy environments.

But AI workloads require different architectures and operating models. Agentic AI, intelligent automation and real-time decision-making depend on data availability and streamlined integration between core insurance systems. If you try to deliver an AI-powered claims experience when customer data is spread across systems that don’t communicate with one another, the potential of AI quickly runs into the realities of the underlying technology.

And nobody is starting with a blank slate. Insurers are introducing AI while managing a complex mix of systems, regulations and operational demands, including:

• Legacy policy administration platforms
• Fragmented claims ecosystems
• Complex regulatory obligations
• Data-intensive operations
• Rising expectations for real-time service

With cloud strategy having a direct impact on what can be achieved with AI, cloud modernization is now being seen as a strategic investment that will help unlock the value of AI through innovation, growth, operational agility and better customer experiences.

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The execution gap is operational

Although insurers understand the strategic importance of AI, they continue to struggle with operationalization. Modernizing applications and data platforms doesn’t happen overnight, and integrating cloud and on-premises environments is no small undertaking.

Our report shows that, globally and across industries, data readiness and analytics challenges are a leading cause of dissatisfaction among organizations trying to build cloud-native AI applications.

This highlights a critical issue: For insurers, AI success depends on enterprise-wide operational alignment, which means they need a new way of coordinating cloud, data and modernization initiatives throughout the business. AI strategies developed independently from modernization programs can create fragmented architectures, duplicated investments and operational bottlenecks.

According to our report, 58% of AI leaders in insurance are likely to rebuild applications with AI embedded at the core rather than rely on bolt-on AI tools or superficial automation. Only 15% of all other organizations in the industry take this approach.

Governance, security and trust are paramount

Insurance is one of the world’s most regulated and trust-dependent industries. AI systems must therefore support explainability, governance, auditability, data protection and operational resilience.

This explains why security, governance, risk and compliance concerns regarding autonomous agents are now the top barrier to insurers’ adoption of agentic AI in cloud-based solutions.

Cloud architecture decisions are becoming increasingly strategic. Where workloads run still matters, especially when private and sovereign cloud enter the picture, but insurers also need to create enterprise-grade AI environments capable of balancing innovation with governance at scale.

All insurance respondents in our research expect their adoption of private cloud to grow, with 45% saying they will have private cloud in place by 2027. This makes insurance one of the industries with the highest expected adoption rates.

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Modernization must become platform-led

Insurers manage multiple business domains simultaneously. Claims operations, underwriting, distribution and customer engagement all depend on shared access to trusted data, interoperable systems and secure execution environments. Without stronger integration between these layers, AI initiatives risk remaining trapped in pilot phases.

As insurers plan for enterprise deployment of AI, they are therefore recognizing the need for more connected, platform-led approaches that bring together cloud infrastructure, AI capabilities, governance frameworks, operational workflows and data ecosystems.

Our report shows that about 1 in 3 are already building centralized, cloud-native platforms to manage the growing demand for AI.

Make cloud the execution layer for AI

As cloud becomes the execution layer for AI in insurance, insurers have to combine modernization, governance, data readiness and AI strategy into a single operational vision.

Only then will they be able to scale AI securely, responsibly and effectively throughout the business, making it the foundation for long-term innovation and growth.