The insurance industry is experiencing a transformative shift with the integration of generative AI, which has the potential to unlock up to $70 billion in value by enhancing various aspects of the insurance life cycle, such as risk assessment and customer service. Despite the enthusiasm, many insurers face challenges, including decision paralysis due to the multitude of potential AI applications and the costs associated with transitioning from proof-of-concept to production. Successful early adopters have utilized AI for tasks like summarization and creating knowledge assistants, which streamline operations and improve decision-making. For instance, companies like Convex have leveraged AI solutions to significantly speed up the underwriting process by extracting and summarizing unstructured data, while AI-powered knowledge assistants help automate claims processes and enhance customer service efficiency. Insurers are encouraged to prepare their IT infrastructure and tailor AI models to their specific needs, employing strategies such as retrieval-augmented generation and semantic search to optimize results. Fine-tuning AI models with domain-specific knowledge is essential for achieving accurate outputs, and insurers are advised to take inspiration from early adopters to strategically implement AI solutions that align with their business objectives.