Primary speaker:
Keshav Raina, Administrative Officer (Data Analyst), PGA, ITS
Description:
AI tools have become more commonplace than ever, and emphasis has shifted from the "what" and "how" of AI to the "when" and "where". Small, thoughtful applications of AI in the right places within workflows often produce far more value than flashy but clumsy overhauls. Yet, in enterprise contexts, what is often lacking is a systematic approach to identifying good use cases and well-defined output verification strategies. The question that needs to be answered is: “When is generative AI actually the right tool for the job?”
In this session, we will explore a set of criteria for identifying when generative AI is indeed the best tool available to us. We will examine what these criteria reveal: how seemingly unintuitive use cases may showcase the strengths of AI, while more obvious applications can carry hidden costs. Many common applications of AI fail these criteria and produce inefficient or unreliable results, leading to frustration and discouragement. By aligning AI use with these criteria, we can achieve more efficient and dependable outcomes. The process of discovering use cases for generative AI often goes from “What can AI do?” to “Where can I apply it in my work?”: solutions looking for problems. Instead, we’ll examine common workflows and explore where AI can be applied to solve real problems and realize efficiencies.
Participants may leave this session feeling more confident in their strategy for incorporating AI into their work. Others may conclude that the criteria miss some considerations or disagree with some of their implications. The conversation at hand is about structured approaches to judging effective AI use in enterprise contexts, and the purpose of this session isn’t to end that conversation — it’s to start one.
