Artificial intelligence (AI) 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.
When the commercial potential of AI started to become obvious about a decade ago, many core applications were well-established prediction problems. For example, lenders used AI to predict whether people would pay back a loan and manufacturers used AI in predictive maintenance to forecast equipment failures. These applications seem quite distinct from what ChatGPT, Gemini, and other AI tools do. Yet under the hood, generative AI tools are still prediction engines, enabled by improvements in computational statistics and large amounts of data.
Failing to recognize that generative tools are merely prediction machines will lead to strategic missteps. Today’s AIs are built from data and do not provide judgment on when and how AIs should be built and used. Data and judgment are fundamental to the application of generative AI, even if the applications seem beyond mere prediction, like writing text. Thus, successful AI deployments depend on having access to relevant data and on having the business judgment to know which AIs will be most effective.
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