By Siddharth Dixit
As insurers adopt AI and scale their digital capabilities, thoughtful cloud planning—and careful cost oversight—has never been more critical.
The digital age has been transforming the insurance industry for years, but the recent surge in artificial intelligence (AI) is now taking center stage. While AI promises substantial benefits, several fundamental challenges remain, including breaking through entrenched legacy systems, proprietary homegrown platforms, and heavily customized off-the-shelf software that insurers use.
One such challenge—often underestimated but financially significant—is the migration to cloud infrastructure to support AI workloads. As insurers shift toward large-scale data analytics and generative AI (GenAI) models, the costs associated with these workloads carry potential complications. Companies must balance the scalability and performance benefits of the cloud with cost optimization strategies. Measuring the tangible impact of new technology initiatives in cost savings and value generation is crucial to demonstrate the return on investment and justify future investments.
Advantages
The advantages of cloud adoption in the insurance industry are increasingly difficult to ignore. Some significant benefits include:
- Cost reduction over time. Despite initial migration and setup costs, organizations often see long-term savings by eliminating the need to manage and maintain their data centers. Legacy systems are complex and can carry high operational costs. Shifting from managing the infrastructure and operations in-house to the cloud can offer financial flexibility, especially when utilizing a pay-per-use model that requires companies to pay only for the resources they consume rather than a flat fee regardless of use.
- Access to advanced technologies. Cloud adoption provides access to the latest hardware and software advancements and computing environments. This includes the latest graphics processing units (GPUs) and AI-optimized chipsets from vendors like Nvidia and scalable infrastructure offered by platforms such as AWS and Microsoft Azure, which are especially crucial for running GPU-intensive AI workloads. These providers also let companies test and deploy models quickly, continuously update their services, and innovate rapidly, allowing insurers to introduce new features without lengthy release cycles and IT timelines. Alternatively, insurers may struggle to integrate legacy systems with emerging cloud-native AI tools and may not be equipped to evolve with rising business needs.
- Scalability. The flexible infrastructure allowed by cloud adoption can provide significant value to companies looking to expand their operations, finance, IT, and other departments. Whether nationally or globally, the cloud enables insurers to leverage the cloud infrastructure to scale their businesses and get to market faster than those in limited-capacity on-premises environments. These capabilities help insurers adapt to the rapidly evolving tech landscape and business needs.
- Security and monitoring tools. Unlike on-premises systems, which may lack automated safeguards, cloud environments offer tools for robust observability and continuous security monitoring. Cloud-based systems can be easier to secure than legacy systems due to built-in observability, real-time alerts, continuous compliance tracking, and other key insights into how the workloads are running. Comparable security measures in an on-premises environment require significantly more effort and manpower.
Challenges and Cost Drivers
Despite its advantages, cloud adoption has considerable challenges, notably in terms of cost. Many employees do not understand the actual value of the cloud, how it will help in the long run, and, perhaps most importantly, the costs associated with this effort. When company workforces do not understand cloud pricing, developers may deploy resources without fully recognizing the associated expenses, resulting in a larger-than-expected bill. This is especially true for those implementing a pay-per-use model. While this approach can save on costs when utilized correctly, expenses can skyrocket if the system is not properly understood and used by those managing large datasets.
Cloud migration itself is a significant cost driver. The process requires far more than a simple “lift and shift” approach to move from the legacy platform to the cloud. Many insurers that operate in an on-premises environment rely on older technology. The expenses and manual work required to move or connect these systems to the cloud, transfer data, and subsequently test the systems require a significant investment. A testing strategy is particularly critical with migration. Legacy systems can be robust, so building a strategy upfront helps ensure the system still functions the way it did before migrating to the cloud and provides the expected business value. It’s essential for company employees, such as those on technical teams, and corporate leadership to understand the upfront costs associated with the transition to cloud platforms.
Too often, these key contributors to the business and those in charge of the decisions fail to avoid a common pitfall: the “cloud first, cost later” mindset. Organizations that migrate too quickly to the cloud without considering and researching the actual operational expenses risk misunderstanding the implications of cloud spending and falling victim to unanticipated costs. This also leads to inefficient resource utilization and wasted effort surrounding the migration.
Software, storage, and network costs are the three foundational cost categories in the cloud. Often, network costs can spiral if the data exchange between systems isn’t optimized. Data retention requirements, regional data laws, and performance demands all contribute to this complexity. Storage costs can differ drastically depending on whether the organization plans to store data temporarily or permanently.
Moreover, because enterprises handle sensitive personal customer information, including health and financial data, it is imperative for insurance companies to implement stringent security and compliance requirements. This is even more vital when moving from on-premises systems to the cloud, which can be more susceptible to data breaches and cyberattacks. Regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) and the handling of personal customer data require investments in encryption, data loss prevention, disaster recovery, and secure backup solutions. These measures are pricey yet critical for protecting and monitoring customer data in a cloud setting during the initial migration and continuously moving forward.
Strategies for Managing Cloud Costs
Strategic planning is essential to realize the financial benefits of cloud adoption. A top recommendation is to create a dedicated FinOps team—a cross-functional group that brings together finance, IT, and business stakeholders to manage and optimize cloud costs—to manage the migration plan, efforts, and spending. Key responsibilities include developing a migration plan, identifying hardware and software requirements, pinpointing inefficiencies, and providing evaluations, estimations, and budgets for associated migration costs and long-term usage of cloud systems. Once the migration is complete, the team monitors and analyzes its success and determines whether the costs align with the initial budget. The team’s efforts help to enforce a cost-conscious culture across the entire process while aligning usage with value.
Frugal architecture laws are another critical strategy for effective cloud cost management. These principles encourage organizations to design cloud environments conservatively and intentionally from the start, focusing on simplicity, efficiency, and only using what’s needed.
From there, it’s important to focus on right sizing the infrastructure, managing assets appropriately, and avoiding complexity and minimizing unnecessary infrastructure.
After moving to the cloud, it’s essential for insurers to invest in observability tools, such as Dynatrace, Datadog, and Splunk, which collect and analyze system logs, performance data, and application behaviors, uncovering inefficiencies that can drive up cloud costs. Additionally, it’s important to monitor the impact of auto-scaling features, which dynamically add computing resources when demand increases. While this optimizes performance and uptime, it can also create unexpected costs if scaling is too aggressive or left unchecked. To manage these risks, AI-driven tools within AWS can forecast demand, flag over-provisioning, and recommend scale adjustments. In addition, predictive modeling plays a dual role: It helps forecast cloud usage patterns and optimizes core insurance operations. These improvements reduce both operational and cloud processing costs.
Additionally, key performance indicators (KPIs) can measure the success of cloud migration and drive any subsequent business decisions. KPIs include cost per policy, application response time, network latency, customer satisfaction data, and other AI-related parameters. Organizations that report these KPIs to leadership teams will be well positioned to validate the impact of moving to a cloud-based platform and associated investments and to make the case for further innovations.
Finally, companies may consider adopting a hybrid or multi-cloud approach rather than a full-scale migration to a single cloud platform. On-premises infrastructure does not have to disappear completely. After assessing which systems could move to the cloud, the organization may determine that it’s best if some core applications stay on-premises for cost and control reasons. If so, a hybrid environment may be the optimal strategy. This requires running core, high-volume operations, such as production workloads, within the company’s data center while migrating smaller tasks, including rules evaluations, file transfers, predictive scoring, and schema checks, to the cloud.
Sticking with one cloud platform has some drawbacks. Rather than restricting the entire workload to a single cloud provider, the company may cherry-pick the most cost-effective services across multiple cloud providers. While some companies may appreciate the flexibility and want to mitigate vendor lock-in, others may avoid the complexity and stick with a single cloud provider.
Case Studies and Real-World Examples
Insurers across the industry are embracing the cloud to stay competitive while managing costs. Some key examples include:
- Chubb, a commercial insurance provider implemented a cloud-native platform to scale its cyber insurance solutions internationally. This allowed Chubb to integrate AI tools for underwriting and claims automation, which resulted in faster decision-making and operational savings.
- MetLife and Progressive Insurance, two of the nation’s top insurance providers, implemented cloud technology to automate their claims management processes, including approvals and payouts, to reduce processing times and increase operational efficiency.
- CNA Insurance’s swift development of its data foundation on Google Cloud sets the stage for delivering value-driven analytics products to business units. The company’s long-term vision is to empower executives, underwriters, and brokers with fast, intuitive access to accurate data to drive smarter decisions.
- MediGap Life, an online insurance aggregator, migrated to the cloud to modernize its customer relationship management (CRM) workflow. As a result, it optimized its internal workflow by reducing process execution time by 88 percent.
- Compunnel collaborated with an insurer focused on optimizing financial overruns and inefficiency. It built a cost optimization framework that reduced the insurer’s costs, reduced idle resources by about 90 percent, strengthened its cost monitoring capabilities, and even reduced CO₂ emissions and energy consumption.
Emerging Solutions
As cloud strategies mature, it’s crucial for insurers to prepare for a rapidly evolving technological landscape. Innovations may drive future cost optimization, such as serverless computing, which allows insurers to pay only for the computing resources they use, which can drastically reduce costs. Containerization lets insurers run microservices across hybrid environments to improve the utilization of resources and reduce expenses. Edge computing brings processing closer to data sources and can be utilized by insurers to reduce lag and data transfer costs.
Other technologies are on the horizon but remain largely speculative in their cost implications for the insurance sector. For example, quantum computing may have a role in advanced modeling and risk analysis, introducing opportunities to reduce time and costs for insurers. Forward-thinking organizations will benefit from monitoring these innovations and emerging trends, assessing their potential future impact, and integrating them if they present opportunities to reduce costs.
The Cloud as a Strategic Asset
While there are benefits to migrating to the cloud, organizations that delay or fail to adopt the cloud face long-term risks. These risks include the potential for operational inefficiencies and failure to implement modern AI technologies, which can unlock significant value for companies. In addition, legacy systems have cost concerns. Insurance carriers operating on legacy platforms may experience IT costs up to 41 percent higher than those that have migrated to the cloud, according to Centric, an international management consulting firm. Many insurers on legacy platforms still rely on mainframe systems or data warehouses that require complex integrations and manual work to connect with modern platforms. Maintaining these systems adds to operational costs and can create technical debt.
The shift to cloud-based solutions requires a significant change for the insurance industry. As insurers embrace this transition, they can reduce costs, enhance operational efficiency, improve flexibility, and facilitate the future incorporation of new and emerging solutions. The cloud is not just a technology upgrade. It’s a strategic asset that will drive innovation and support the long-term success of the insurance industry.
Siddharth Dixit is a principal architect for a specialty insurance carrier, where he architects and implements robust cloud infrastructure and DevOps solutions, pioneers serverless machine learning solutions, and conducts training sessions on cloud migration. Specializing in cloud infrastructure, cybersecurity, and enterprise architecture, he has successfully led large-scale cloud migrations and built AI-ready data engineering platforms.