8 MAY JUNE 2024By Michael Mocanu, Sr. Director, Technology Data Science & Data Governance, Liberty Mutual InsuranceEFFICIENT AI TEAMS IN THE AGE OF GENERATIVE AIAs the analytics landscape has matured over the last two decades, the topic of how to build an efficient AI team in the financial services sector has become a priority from the C-suite on down. Today, organizations are embracing AI, such as the promise of generative AI and large language models (LLMs), and thinking about the right size investing in the mix of people, tools, roles, and collaborations. Generative AI is set to touch every aspect of financial industry operations, from sales to customer service. The need for timely feedback and iterative progress in implementing the novel creative process of generative AI means that the speed, breadth, and magnitude of team collaborations have to be greatly expanded with efficient teams. New AI capabilities comprise prompt engineering to output novel ideas and designs, incorporating custom data into private LLMs for conditional generation, and choosing a balanced architecture with privacy and ethical considerations. Having an efficient AI team will determine the value of the content generative AI can create in the financial services sector.Back to the Future: AI Teams Than & NowA decade ago, the trend for many financial firms was to have Analytics Center of Excellence (CoE) representing significant dollar investments. New CoEs would spring up, sometimes comprising hundreds of talented data scientists. Fast forward, and CoEs are mostly gone, replaced by Amazon's "2 pizza" teams, Spotify's squads, Airbnb's `autonomous' teams, and Uber's ML teams, among others. These new team structures are small, highly dynamic, creative organisms that often have less than ten people in diverse roles while taking innovation to new heights.What forced AI/ML teams to change?Looking at AI team changes begs the question of why the metamorphosis, given that organizational goals have remained unchanged. The simple answer is technology. The CoE focus was on tools due to the large upfront investments in foundational technologies, which required ample support from the engineering team along with the dedicated large analytics team. This choreographed cast of talent was built on delivering key results on the same scale as the financial outlays ­ which often it did not. This paradigm broke down once the Xaas cloud revolution started with open source and web services. This new reality is fueled by the machine learning, AI, and data (MAD) landscape, with hundreds of domains served by thousands of firms vying to offer pay-as-you-go services.IN MY OPINIONIN MY OPINIONIN MY OPINION
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