Implementing AI in insurance: Four strategies for overcoming common roadblocks 

Across the insurance industry, organizations are under enormous pressure to deliver a seamless digital experience for customers, while at the same time automate processes, modernize legacy systems, and cut costs. Artificial intelligence (AI) is being touted as a magic bullet—a disruptive technology that will help alleviate these pressures while improving the customer’s digital experience. 

It’s true that AI has tremendous automation potential. For example, chatbots are supporting underwriting, sentiment analysis is driving lead management, and virtual experts are managing claims data. In addition, generative AI, machine learning (ML), and large language models (LLMs) are changing how customers meet their insurance needs. While AI offers significant opportunity for innovation and transformation, successful implementation requires a meaningful commitment from leaders coupled with significant investment in strategic planning.

According to the 2023 CGI Voice of Our Clients research, AI and ML are the leading innovation areas over the next three years in both the property and casualty and life and pension sectors. At the same time, however, AI transformation in the insurance industry faces several challenges, including innovation hesitation, data silos, regulations, legacy technology, and privacy concerns. (Read more in Forbes’ coverage: The Evolution Of Insurance In An AI-Driven World.)

While overcoming these hurdles will be an ongoing effort, there are four ways your organization can manage and accelerate your AI transformational investment:

1. Conduct a feasibility study

A thorough analysis of existing legacy systems can help you identify gaps and areas for improvement. Carefully analyzing the technical, economic, operational, and legal impacts of AI leads to more informed decision-making and achievable ROI expectations. As part of your feasibility study, honestly evaluate the leadership commitment to implementing AI processes and technologies within your organization. Successful implementation depends on top-level management engagement and a holistic view beyond technology, including your organization’s approach to the responsible use of AI. 

2. Perform an architectural assessment

Effectively integrating AI into existing legacy systems is an industry-wide challenge. AI solutions require compatibility and seamless interaction with your existing architecture and platforms; however, many legacy systems may not be designed to accommodate AI capabilities. Taking the following steps early in the strategic planning process can help ensure your legacy systems are compatible with emerging AI capabilities:

• Perform a compatibility analysis: Assess the compatibility of your legacy systems with AI integration and identify potential areas of conflict and incompatibility.
• Enhance data quality: Because high-quality data is essential for effective AI integration, improve the data quality within your legacy systems by introducing data governance.
• Implement AI middleware: AI middleware acts as an intermediary between AI systems and legacy systems, facilitating seamless communication.
• Implement APIs and microservices: Ensure that APIs and microservices are in place to enable AI systems to interact effectively with legacy systems. 
• Design a scalable architecture: Build a target architecture that can scale to accommodate AI technologies while aligning with your overall AI vision.

3. Perform a cloud readiness assessment

Transitioning to the cloud is crucial for AI-based transformation, so assessing your cloud readiness and ensuring a smooth migration is essential. Consider the following recommendations:

• Leverage cloud-based AI services: To simplify integration and reduce the need for on-premises hardware upgrades, leverage cloud-based AI services and platforms that can interact with legacy systems via APIs. 
• Explore AI-as-a-Service (AIaaS): Investigate AIaaS solutions offered by cloud providers that can be easily integrated with existing systems while meeting your data security and privacy requirements and standards.
• Consider cloud integration: Various cloud platforms on the market host and scale AI workloads, as well as offer AI tools and services.

4. Implement a change management strategy

Successful transformation extends beyond technology, requiring a cultural shift within the organization. Because resistance to change can hinder AI implementation, we recommend investment in the following three areas:

• Comprehensive change management: Implement a comprehensive change management strategy that promotes agility, adaptability, and innovation throughout the organization.
• Skill enhancement: Invest in skill enhancement for your IT teams to ensure they can effectively manage and operate the newly modernized systems.
• Change management models: Synthesize change management models to facilitate a smooth transition and gain employee buy-in.

Helping our insurance clients unlock the potential of AI 

There’s no doubt that, within the next few years, AI and ML will transform the customer experience and front- and back-office processes, as well as move into all areas of operations across the insurance industry. Considerable investment in readiness assessment and change management is required for an insurer to operate successfully in this shifting ecosystem. 

Our white paper, Data-driven insurance: A path to strategic advantage, offers best practices for optimizing your organization’s data-driven insurance strategy, which is key to successful AI implementation. Download it to learn more, and feel free to contact me to explore how we can support your organization’s digital and AI transformation at all stages—from planning to implementation to ongoing evolution and managed services.