If you fail to understand the fundamental nature of these tools, you’ll inevitably use them incorrectly.
November 18, 2024
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To understand the strategic implications of AI’s new capabilities, managers need a framework for when AI will be helpful and when it might fail. Under the hood, generative AI tools are still prediction engines, enabled by improvements in computational statistics and large amounts of data. Using AI well requires understanding that today’s AI uses data to make statistical predictions and it’s up to humans to provide judgment about when and how AI should be used. This has not changed with generative AI. Its applications depend on data. Also, judgment is integral to the selection of data, the training of models, and the overall implementation.
Artificial intelligence tools can now write, code, draw, summarize, and brainstorm. The proliferation of generative AI tools poses serious questions for managers, such as: What tasks can be done by AI, what will humans still need to do, and what are the sustainable sources of competitive advantage as AI continues to improve? To understand the strategic implications of these new capabilities, managers need a framework for when AI will be helpful and when it might fail.
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Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management. He is the founder of the Creative Destruction Lab, founder of Metaverse Mind Lab, co-founder of NEXT Canada, and co-founder of Sanctuary. He is also a co-author of Power and Prediction: The Disruptive Economics of Artificial Intelligence (Harvard Business Review Press, 2022).
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