How Can Human-Computer “Superminds” Develop Business Strategies?

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The Future of Management in an AI World

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Abstract

Many people might think that strategic planning is one of the most difficult places for artificial intelligence—and computers more generally—to be useful in management. Tom Malone highlights that corporate strategic planning provides a useful illustration of several broad lessons about how computers are likely to be used in business. First, what matters most is not how smart the computers are alone but how smart the humans and computers are together. Second, in many cases, the most valuable contributions of computers will not just be their artificial intelligence but also their ability to provide hyperconnectivity—connecting people to other people in rich new ways. And finally, the most important uses of computers are not likely to be in replacing humans but in allowing people and computers to do things better together than either could do alone.

This chapter is adapted from Thomas W. Malone, Superminds: The Surprising Power of People and Computers Thinking Together (New York: Little Brown, 2018; London: Oneworld Publications, 2018) and Thomas W. Malone, “How Human-Computer ‘Superminds’ Are Redefining the Future of Work”. MIT Sloan Management Review, 2018, 59 (4): 34–41. Reproduced with permission of the Licensor through PLSclear.

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Notes

  1. 1.

    David Ferrucci, e-mail to the author, August 24, 2016. Ferrucci led the IBM team that developed Watson.

  2. 2.

    Oil of Olay and all other products named here are trademarks of Procter & Gamble.

  3. 3.

    P&G sold the Pringles business to Kellogg in 2012, so this would no longer be a P&G product. For a description of the invention of the process for printing on Pringles, see Larry Huston and Nabil Sakkab, “Connect and Develop: Inside Procter & Gamble’s New Model for Innovation.” Harvard Business Review, March 2006, reprint no. R0603C, https://hbr.org/2006/03/connect-and-develop-inside-procter-gambles-new-model-for-innovation.

  4. 4.

    For an overview of Bayesian networks written for a general audience, see Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2015), chapter 6.

    Bayesian networks are often difficult to use at large-scale, but there are numerous technical approaches to doing so. One that seems particularly promising for applications like those described here is Markov Learning Networks (MLNs) because they allow people to specify many kinds of rules for the likely logical relationships among events without having to estimate detailed conditional probabilities (see Domingos, The Master Algorithm, chapter 9).

  5. 5.

    Martin Reeves and Daichi Ueda use the term integrated strategy machine to describe a somewhat similar idea. But unlike their article, the focus in the present article is more on how large numbers of people throughout the organization and beyond can be involved in the process and on the specific roles people and machines will play. See M. Reeves and D. Ueda, “Designing the Machines That Will Design Strategy,” http://hbr.org.

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Malone, T.W. (2020). How Can Human-Computer “Superminds” Develop Business Strategies?. In: Canals, J., Heukamp, F. (eds) The Future of Management in an AI World. IESE Business Collection. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-20680-2_9

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