Editor's Synopsis
MIT Sloan School of Management shares a concise three-step framework for identifying practical use cases for large language models (LLMs) in organizations. The recommended approach involves breaking down existing workflows into discrete tasks, carefully evaluating all costs associated with automation (not just implementation but ongoing expenses), and launching pilot programs to test and validate before scaling.
This guidance reflects a growing emphasis on structured, cost-conscious adoption of AI rather than rushing to deploy LLMs broadly. By focusing on task-level analysis and pilots, organizations can better match LLM capabilities to genuine business needs and avoid costly missteps.