Generative AI is here. Are you ready?

Once the use cases have been identified, businesses must select the appropriate GenAI tools and models for implementation, embed the GenAI team across functions, and provide training on prompt engineering. Finally, businesses must define value measurement metrics, set up monitoring tools, review, and audit, and collect feedback to improve continuously. How do we unlock full potential of GenAI to deliver across insurance enterprise?

Incubation and Identification

Monitoring and adoption

Impact Generation

Tool integration

Orchestration

• Identifying business objectives where GenAI can provide improvements • Identify relevant functions to kick start training and adoption of GenAI • Use cases prioritization leveraging 4E (Economics, Exposure, Edge and Extensibility) framework: • Economics -> Revenue, Productivity, Costs • Exposure -> Reputation risk, compliance risk, Privacy & Security • Edge -> Cool factor, Brand impact, Novelty • Extensibility -> Concept and tech extension

• Selection of GAI

• Infrastructure check: (Open AI, Azure Open AI, Open source LLMs ) • Maturity of LLMs from a commercial standpoint - Licensing, Vendor selection, Compliance check • Customized inferencing

• Define metrics such as the number of AI models deployed, the amount of data processed by AI models • Set up monitoring tools to track and collect data on the defined metrics • Review and audit metrics • Conduct audits of the models to ensure that they are being used ethically and effectively • Collect feedback to continuously improve

• Value to

Customers

tools and models for implementation – API integration and orchestration implement GenAI tools for UW, Claims, etc. owners to embed use of tools

• Value to

Enterprise

• Embed GenAI team across functions to

• Functional

using client data

• Transfer learning/

• Training on Prompt engineering • Monitoring,

Fine-tuning of models based on custom data

implementing data privacy measures

GAI Governance Council + Ethics Committee

GenAI Centre of Excellence GenAI Core team

Fig 3. Operating model

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