{"slug":"marsh-mclennan-saves-over-1-million-hours-with-agentic-ai-assistant-lenai","url":"https://findausecase.com/use-cases/marsh-mclennan-saves-over-1-million-hours-with-agentic-ai-assistant-lenai","title":"Marsh McLennan saves over 1 million hours with agentic AI assistant LenAI","description":"Professional services firm Marsh McLennan built LenAI, an AI assistant now used by its roughly 90,000 employees for around 20 million requests annually. To fix intent-recognition problems in the original GPT-3.5-based assistant, Marsh McLennan worked with Predibase to fine-tune a Llama-3.1-8B-Instruct model using LoRA and Turbo LoRA, improving accuracy by 10-12% over GPT-3.5 and 7% over GPT-4o-mini, and cutting response time below 4 seconds. Since its December 2023 launch, LenAI has handled over 25 million cumulative queries and saved more than 1 million hours of team time in its first year.","company":"Marsh McLennan","industry":"Financial Services","aiCapabilities":["Agentic AI","Large Language Models"],"technology":["Predibase","Llama 3.1-8B-Instruct","LoRA","Turbo LoRA"],"deployment":"Unknown","problemStatement":"Marsh McLennan's original AI assistant, LenAI, was built on GPT-3.5 and occasionally faced difficulties with intent recognition — a crucial capability for agentic AI — sometimes misinterpreting the correct tool based on a user's query; for example, when users requested simple tasks like drafting emails, the model would return documents containing the keyword 'email' rather than launching the email client.","solutionApproach":"To reach the next level of accuracy and drive task automation with higher precision, Marsh McLennan worked with Predibase to fine-tune a Llama-3.1-8B-Instruct model using low-rank adaptation (LoRA) and Predibase's Turbo LoRA, deploying on a GPU that supports FP8 quantization.","businessValue":"The fine-tuned Llama-3.1-8B-Instruct model improved accuracy by 10-12% over GPT-3.5 and 7% over GPT-4o-mini, and reduced round-trip request time to below 4 seconds. Since its December 2023 launch, LenAI has supported over 25 million cumulative queries across the organization and saved at least 1 million hours of team time in its first year alone; the company now handles around 20 million requests annually, with upwards of 90,000 people using it.","evidence":{"band":"high"},"sourceUrl":"https://predibase.com/blog/how-marsh-mclennan-saved-over-1-million-hours-of-team-time-with-agentic-ai","dates":{"publishedAt":"2026-08-16T15:52:48.531Z","publishedAtSource":"ledger","updatedAt":"2026-08-16T15:52:48.531Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/marsh-mclennan-saves-over-1-million-hours-with-agentic-ai-assistant-lenai. Bulk republication requires permission."}