ChatGPT and Other Large Language Models: Life Insurance Edition

Create a vendor selection project
Click to express your interest in this report
Indication of coverage against your requirements
A subscription is required to activate this feature. Contact us for more info.
Celent have reviewed this profile and believe it to be accurate.
We are waiting for the vendor to publish their solution profile. Contact us or request the RFX.
Projects allow you to export Registered Vendor details and survey responses for analysis outside of Marsh CND. Please refer to the Marsh CND User Guide for detailed instructions.
Download Registered Vendor Survey responses as PDF
Contact vendor directly with specific questions (ie. pricing, capacity, etc)
1 March 2023

What is Coming Next


The launch of ChatGPT in November of 2022 by OpenAI and Microsoft has brought an acute and immediate focus on the impact of large language models (LLMs) to our industry. The question for insurers is what is the potential impact and how will LLMs be implemented. ChatGPT and other LLMs represent augmented intelligence tools, which allow us to combine artificial intelligence (AI) with human intelligence to enhance and amplify human abilities, e.g., at a base level, generate content and get answers quickly, and at a higher level, improve our decision-making, problem-solving, and overall cognitive abilities.

Importantly, LLMs have a simple, user-friendly interface that allows anybody with internet access to use it which has increased public interest and willingness for adoption. Tools that deliver augmented intelligence have the potential to be transformational—and to improve or add value to many tasks undertaken by a person. LLMs will prompt organizations to reflect on the true value provided by human employees and may ultimately result in modified talent requirements. Depending on cost efficiency, the playing field could level with any size enterprise accessing LLMs via an API.

For insurers the risk of doing nothing with LLMs exists because the competitive gap established by early adopters could be sustainable due to an LLM’s inherent ability to learn and improve. Consumers will likely lead adoption, raising their service expectations across industries forcing insurers at some point to implement an LLM solution. Organizations that don't may be less operationally efficient than peers using LLMs, which may lead to long-term challenges and diminished profitability.

For any industry, adoption of augmented intelligence tools requires mapping unchartered waters and extensive collaboration across enterprise stakeholders and regulators.