Identifying Ideal Business Use Cases for Generative AI

Generative AI offers potential advantages for businesses but also presents challenges, including inaccuracies and logical difficulties. To effectively leverage this technology, organizations can follow a three-step approach outlined by MIT Sloan professor Rama Ramakrishnan during a webinar. First, they should break down workflows into discrete tasks to identify those suitable for automation. For example, some educational tasks can be automated, while others may not translate well. Second, companies must assess the generative AI cost equation, considering both obvious and hidden costs associated with automation. Third, they should pilot LLM-based applications while maintaining a rigorous evaluation process to ensure effectiveness. Best practices include careful monitoring, focusing on targeted use cases, and training internal talent. Ramakrishnan emphasizes prioritizing tasks with quick return on investment to learn from early implementations, advising organizations to start small and scale up as AI capabilities improve.

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