AI tools remain prediction engines despite new capabilities, requiring both quality data and human judgment for successful deployment, according to new analysis. While generative AI can now handle complex tasks like writing and coding, its fundamental nature as a prediction machine means organizations must understand its limitations and provide appropriate oversight, argue Ajay Agrawal (Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management), Joshua Gans (Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School, and the chief economist at the Creative Destruction Lab), and Avi Goldfarb (Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School) in a piece published on Harvard Business Review. Poor data can lead to errors, while lack of human judgment in deployment can result in strategic failures, particularly in high-stakes situations. An excerpt from the story: Thinking of computers as arithmetic machines is

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