{"id":13207,"date":"2025-04-04T06:29:36","date_gmt":"2025-04-04T14:29:36","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?page_id=13207"},"modified":"2025-06-12T16:55:19","modified_gmt":"2025-06-12T16:55:19","slug":"carolyn-olsen","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/convergence\/carolyn-olsen\/","title":{"rendered":"Carolyn Olsen"},"content":{"rendered":"\n
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\u2190 BACK TO EVENT PAGE<\/a><\/p>\n\n\n\n

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\"Carolyn<\/figure>\n<\/div>\n\n\n\n
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Carolyn Olsen<\/h1>\n\n\n

VP of Data Science at Clearcover<\/h2>\n<\/div>\n\n\n\n
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Carolyn Olsen is the Vice President of Data Science at Clearcover, where she leads teams developing innovative AI\/ML solutions for the insurance industry. Since early 2023, she has led multiple production GenAI implementations, including a claims adjuster assistant bot and a generative AI digital statement collection bot that significantly improves claims processing efficiency. With extensive experience in ML leadership, Carolyn specializes in aligning advanced AI technologies with business objectives to deliver measurable ROI. Her work focuses on practical applications of LLMs and ML that enhance operational efficiency while addressing the unique challenges of AI\/ML deployment in regulated industries.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

May 13th, 1:05 \u2013 1:35PM ET<\/p>\n\n\n\n

Simpler, Faster, Better: Challenging GenAI Implementation Assumptions<\/h2>\n\n\n\n
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When Clearcover\u2019s ML team began implementing generative AI solutions in early 2023, we had assumptions about which technical approaches would deliver value. After successfully deploying multiple production GenAI applications, including a claims adjuster assistant and a claim statement collection bot, the reality surprised us. This talk dissects the gap between GenAI theory and practice, sharing concrete examples of approaches we thought we\u2019d need versus what actually worked. I\u2019ll explore why certain \u201cexpected\u201d implementation techniques like vector search and fine-tuning may not always be necessary, and how to determine when simpler alternatives suffice. The presentation will cover our journey from proof-of-concept to production, highlighting counterintuitive discoveries around model hosting, prompt engineering, and API integration that ultimately led to scalable solutions within months rather than quarters. Attendees will gain practical frameworks for navigating their own GenAI implementations, learning from our experiences so they can deliver measurable business value while avoiding unnecessary complexities that can delay deployment and adoption.<\/p>\n\n\n\n

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