Artificial intelligence: From mystery to mastery

Think Use Case instead of Technology

Almost 40% of all practitioners who have not yet invested in AI don’t know what AI can be used for in their business. In order to exploit relevant opportunities to develop an intelligent insurance, practitioners must face the challenge of identifying concrete applications for AI in the form of use cases.

Identify the right Use Cases to leverage AI in your business

Drawing on the analysis of applications of AI in the insurance industry as well as innovative consumer-led industries, this paper outlines a framework of four value areas in which AI creates business value. Using our specific Use Case Formula derived from almost 200 AI applications, it provides the essential groundwork to structure and guide the thinking and identification of relevant use cases.

Insurers have yet to unlock the full potential of AI

By mapping prominent insurance case studies to our framework, the paper confirms that the majority of applications realized by incumbents today focus on optimizing existing services and processes. These efforts are already yielding tangible benefits. However, insurers are lagging behind in leveraging AI to discover new insights in operations and customer interactions. In order to uncover the possibilities, the paper describes leading case studies from other industries and shares recommendations on where to look next.

Act now

For those who are yet to embark on their journey towards AI in insurance, it is key to start investing in Artificial Intelligence now. For leaders looking to steer their organizational ships into the auspicious waters of AI and start investing in AI, the following guiding principles apply:

1. Assess the data at hand and accessible from the outside to get an initial idea of potential use cases, leveraging a bottom-up approach 
2. Start small with applications on the lower spectrum of complexity and iteratively scale to more complex solutions as you grow your expertise
3. Don’t be afraid of failure as AI applications are prone to various challenges and the key to success is to rather fail fast than to burn resources on non-promising projects