McKinsey estimates that AI can create new economic value equal to almost 25% of revenue for individual insurers. More suppliers than ever before are offering gamechanging AI models. We all get it, AI will revolutionize the life and annuity industry. The key word being “will.” Whether you’ve taken the plunge or have been observing from the sidelines, few successful, scalable AI use cases exist. Like many other stages in our industry’s technological journey, AI is suffering from a common challenge – operating in harmony with legacy systems.
What’s the point of AI if it is based on incomplete data or it is unable to drive action? The AI value chain is only as strong as its weakest link. We’ve identified three foundational challenges that if successfully addressed can help to release the full potential of AI.
Bridging to Your Legacy System Data
Impactful AI strategies require a continuous flow of up-to-date information to train and retrain models. This is where previous generations of data warehousing strategies break down because they were not designed to support digital experiences. To properly support AI models, both structured and unstructured data must be collected from the various systems of record then transformed into usable formats. Providing AI models complete, timely data ensures that they have all the necessary inputs to effectively make decisions and direct behaviors.
Building Real-Time Workflows
Frequently, the limitations of legacy systems result in the delivery of legacy digital experiences. Even if an AI Model has the potential to efficiently direct operational behaviors, they are only effective if such behaviors can be executed across legacy systems. Within a single digital experience, there can be multiple AI-triggered actions. Successful execution of AI models requires fault-tolerant orchestration of multi-step workflows across all systems of record and digital experiences.
Protecting Your Company
For many carriers, the evolving regulatory perspective on AI is a roadblock to their AI advancement. Building solutions to include both the basics of access security controls and the layers of encryption required (both at rest and in transit) is one step towards maintaining a secure and compliant AI development environment. To fully ensure the safety of data, AI development environments must also support de-identified and obfuscated data when appropriate. Furthermore, a comprehensive audit trail of training data and workflow execution data on model versions must be maintained for each step in every journey.
There’s comfort in knowing that these challenges are not unique to insurance. Innovation leaders, including Microsoft have come to realize the necessity of building an enterprise-grade foundation in order to maximize the potential of AI. Sureify, has been working with carriers to solve this issue for life and annuity insurance. Our CoreCONNECT platform addresses the gap between legacy systems and the vibrant ecosystem of AI products and services entering the market.
Interested in learning more? Read our AI Solutions Powered by CoreCONNECT white paper.