Optimalex at the MPLA CEO/COO Meeting
February 2025
Optimalex, a proud Premium Partner of the Medical Professional Liability Association, sponsored the MPLA Board Governance Roundtable and the MPLA CEO/COO Meeting in Scottsdale AZ (Cesars Republic) on February 3-5, 2025.
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👍🏼 Congrats to Eric Anderson, Michael Stinson, Ginny McGuinness, Bill Burns, Kwon Miller, Leah Deitrick, Jenna Hummell, Becky Ta and all the fantastic team members at Medical Professional Liability Association whom our team at Optimalex (Frank Giaoui, CEO of Optimalex and Luv Aggarwal, Head of Engineering in the picture) is delighted to work with.
They produced an excellent event for board members, CEOs and COOs at member organizations.
Some meaningful insights gleaned on the role of AI in workflow automation, data structuring, and predictive analytics:
💰 The MPL industry is slowly consolidating with the top 10 players accounting for just a little less than 60% of the market share in 2023 compared to 49% in 2014. This is low compared to other segments of liability insurance and such a trend is not likely to change anytime soon. Absent significant economies of scale through organic growth or acquisitions, carriers are exploring other sources of operational efficiency.
🤔 AI and data are viewed as major enablers to empower stakeholders, streamline workflows and deliver a better service. 25% efficiency gains on mundane tasks will free as much time for the management to focus on real value added tasks. The CEO and their C-suite should address such key questions as: How should our AI strategy meet the various stakeholders’ expectations? What particular technology should we invest in? How can we measure the performance before and after deployment? What new liability does that imply for our company?
🧠 At the moment, most products add GenAI features. Next, will be to design products embedding Gen AI from the ground up. Then we’ll move towards "AI Gentic", AI powered bot agents anticipating your next actions and actually performing it for you. It starts now!
💡 It starts now but it’s often a stretch on limited resources, namely data and training capacity. Corporate users are now asking to keep their data on premises or in their cloud while having access to the AI. One good way to keep your data is to create your own environment on GenAI with your own content, a private data set and the generation of your private results.
🤔 How do you use AI to structure (internal and external) data in your workflows and your predictive analytics today? How are you planning to use AI and data in the next couple of years?
If we didn't catch up in person during the conference, contact us and let’s do it online!