PriyaPatel
TLDR:
Insurance companies are implementing generative AI (gen AI) use cases but many are stuck in the pilot phase. McKinsey partners discuss the value of combining gen AI with traditional AI and robotic process automation. Key points include:
- Gen AI has potential economic benefits of $4.4 trillion globally.
- Promising gen AI applications in insurance include extracting insights from unstructured data, generating creative content, and automating client engagement.
Organizations get stuck in the pilot phase due to misplaced focus on technology, lack of impact in use cases, and isolated initiatives. To successfully scale, organizations should reimagine domains and combine gen AI with other technologies to drive meaningful change.
Setting up and developing gen AI capabilities require a strategic vision, a roadmap, usable data, accelerated use case development infrastructure, talent, and the right operating model.
Insurance carriers should focus on data management, data privacy, security, accuracy, and regulatory compliance to avoid risks associated with gen AI implementation, such as bias and ethical concerns.
Full Article:
Despite advancing with gen AI use cases, many insurance companies are stuck in the pilot phase. McKinsey partners discussed the value of combining gen AI with traditional AI and robotic process automation to drive meaningful change and escape “pilot purgatory.”
Gen AI has potential global economic benefits of $4.4 trillion and promising applications in insurance, including extracting insights from unstructured data, generating creative content, and automating client engagement. Organizations often get stuck in the pilot phase due to misplaced focus on technology, lack of impact in use cases, and isolated initiatives.
To successfully scale gen AI, organizations should reimagine domains such as claims, underwriting, and distribution, and combine gen AI with traditional AI and robotic process automation. Setting up and developing gen AI capabilities require a strategic vision, a roadmap, usable data, accelerated use case development infrastructure, talent, and the right operating model.
Insurance carriers should focus on data management, data privacy, security, accuracy, and regulatory compliance to avoid risks associated with gen AI implementation, such as bias and ethical concerns. European regulations, such as the EU Artificial Intelligence Act, provide standards for implementing gen AI in a secure and customer-friendly way.
