Companies need to employ these disciplines to have success with gen AI

    Generative AI is all the rage, and with good reason, but determining when—and whether—these tools are really of value requires considerable effort. 

    As Harvard Business Review writes, some observers are beginning to question whether gen AI will produce enough value to exceed its costs. But extracting economic value from gen AI requires several different types of disciplined capabilities. 

    Here are some of the disciplines companies need to have—or build—before becoming successful with gen AI:

    • Behavioral change: Gen AI requires people to change their behavior. They need to learn whether to use gen AI tech at all during various stages of the content creation process, to use it at the right times and for the right purposes, to accompany it with their own contributions, and to confirm that the resulting output is of merit. 
    • Controlled experimentation: Leaders can’t take for granted that gen AI tools will always improve the quality of output or boost productivity; it’s likely to work for some tasks and applications but not others. 
    • Measurement of business value: Related to experimentation is the discipline of measuring different forms of business value. Individual-level productivity is the easiest form of value to measure with gen AI adoption and, according to some analysts, the quickest return on investment. 

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