Many executives, intent on understanding and exploiting AI for their companies, travel to Silicon Valley to acquaint themselves with the technology and its many promises. These pilgrimages have grown so common that tours now exist to facilitate inside peeks at innovative startups. Buoyed by hype and smatterings of algorithmic knowledge, returning executives share a common goal: determining what products, services, and processes AI can enhance or inspire to sharpen competitive edges. They believe a comprehensive strategy for AI is essential for success.
That well-intentioned belief is off the mark. A strategy for AI is not enough. Creating strategy with AI matters as much — or even more — in terms of exploring and exploiting strategic opportunity. This distinction is not semantic gamesmanship; it’s at the core of how algorithmic innovation truly works in organizations. Real-world success requires making these strategies both complementary and interdependent. Strategies for novel capabilities demand different managerial skills and emphases than strategies with them.
Machine learning pioneers — Amazon, Google, Alibaba, and Netflix come to mind — have learned that separating strategies for developing disruptive capabilities from strategies deployed with those capabilities invariably leads to diminished returns and misalignments. Not incidentally, these organizations are intensely data- and analytics-driven. Their leaders rely heavily on metrics to define, communicate, and drive strategy. This reliance on quantitative measures has increased right along with their growing investment in AI capabilities.
Our research strongly suggests that in a machine learning era, enterprise strategy is defined by the key performance indicators (KPIs) leaders choose to optimize. (See “About the Analysis.”) These KPIs can be customer centric or cost driven, process specific or investor oriented. These are the measures organizations use to create value, accountability, and competitive advantage. Bluntly: Leadership teams that can’t clearly identify and justify their strategic KPI portfolios have no strategy.
In data-rich, digitally instrumented, and algorithmically informed markets, AI plays a critical role in determining what KPIs are measured, how they are measured, and how best to optimize them. Optimizing carefully selected KPIs becomes AI’s strategic purpose. Understanding the business value of optimization is key to aligning and integrating strategies for and with AI and machine learning. KPIs create accountability for optimizing strategic aspirations. Strategic KPIs are what smart machines learn to optimize.
1. In fact, Rockefeller’s ability to obtain railroad rebates was a significant source of competitive advantage. These rebates were eventually deemed unfair to competitors and contributed to the breakup of Standard Oil. See D.A. Crane, “Were Standard Oil’s Railroad Rebates and Drawbacks Cost Justified?” Southern California Law Review 85, no. 3 (March 2012): 559-572.
2. J. Hermann and M. Del Balso, “Scaling Machine Learning at Uber With Michelangelo,” Uber Engineering, Nov. 2, 2018, https://eng.uber.com.
4. M.E. Porter, “What Is Strategy?” Harvard Business Review 74, no. 6 (November-December, 1996): 61-78.
5. See, for instance, A. Agrawal, J. Gans, and A. Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence (Boston: Harvard Business Review Press, 2018).
6. Q.C. Nguyen, D. Li, H.W. Meng, et al., “Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity,” JMIR Public Health Surveillance, no. 2 (Oct. 17, 2016): e158.
7. For more details on how McDonald’s, GoDaddy, and others use machine learning to optimize KPIs, see M. Schrage and D. Kiron, “Leading With Next-Generation Key Performance Indicators,” www.sloanreview.mit.edu, June 26, 2018.
8. “Closing the Corporate Gap on AI,” Forbes Insights, Sept. 21, 2018.