AI transformation brings both opportunities and challenges, particularly in communication between diverse teams. Projects often fail not because of technical issues, but due to language barriers and misunderstanding among stakeholders. The article explores the nuances of terms like 'performance,' 'explainability,' and 'risk,'
Table of contents
Setting the StageWhen Silos CrumbleLendAssist: Illustrative ExampleExpertise Paradox in AI AdoptionMaintaining Credibility — “I still know what I’m doing”Embracing Change: “I need to adapt to AI”Preserving Value: “My experience matters”Performance — A Multifaceted ChallengeExplainability — The Black Box DilemmaRisk — Navigating UncertaintyHidden Psychology of Risk When Talking about AIThe Path Forward: A Practical PerspectiveConclusionFurther reading and citationWhy AI Projects Fail and How They Can SucceedKeep Your AI Projects on TrackSuperagency in the workplace: Empowering people to unlock AI’s full potentialSort: