In part two of this series on adopting new technologies, we dive into a realistic scenario that explores how cross-functional teams can effectively vet tools such as LLMs and Generative AI. Following a university director’s journey to investigate AI’s benefits and risks, the article highlights the necessity of a formal team charter and executive buy-in. By leveraging diverse expertise—from engineering to industrial process modeling—the team identifies critical risks, such as "hallucinations," that could impact donor relations. Ultimately, the story demonstrates how disciplined, collaborative research leads to strategic recommendations that prioritize institutional reputation over media hype.
Not-for-profits are increasingly interested in how Large Language Models (LLMs) and Generative AI can help them achieve more with finite resources. This Connections article explores the use of cross-functional "improvement teams" to vet these novel technologies. By leveraging a formal charter, senior-leader champions, and subject matter experts, organizations can move past the hype to find objective, data-driven solutions for their specific missions
A manager's experience reveals that team success hinges on navigating different "life stages." Initially, an experienced, high-performing team was a "well-oiled machine" that quickly left for promotions. This led to a team of all "newbies" who, despite needing intense training, brought vital fresh energy and innovative process improvements. The author now favors the "Half and Half" approach—a mix of tenured and new staff—which balances institutional knowledge with fresh perspectives. However, it requires intentional effort to ensure equitable development. Discover why managing these different compositions—from high-speed efficiency to urgent training needs—is the key to sustained success in a constantly changing environment.