Improving strategic execution with machine learning – Michael Schrage, David Kiron

MIT Sloan Management Review Executive Editor David Kiron

David Kiron, Executive Editor, MIT Sloan Management Review

Michael Schrage, Research Fellow, MIT Center for Digital Business

From MIT Sloan Management Review

Machine learning (ML) is changing how leaders use metrics to drive business performance, customer experience, and growth. A small but growing group of companies is investing in ML to augment strategic decision-making with key performance indicators (KPIs). Our research,1 based on a global survey and more than a dozen interviews with executives and academics, suggests that ML is literally, and figuratively, redefining how businesses create and measure value.

KPIs traditionally have had a retrospective, reporting bias, but by surfacing hidden variables that anticipate “key performance,” machine learning is making KPIs more predictive and prescriptive. With more forward-looking KPIs, progressive leaders can treat strategic measures as high-octane data fuel for training machine-learning algorithms to optimize business processes. Our survey and interviews suggest that this flip ― transforming KPIs from analytic outputs to data inputs ― is at an early, albeit promising, stage.

Those companies that are already taking action on machine learning ― investing in ML and actively using it to engage customers ― differ radically from companies that are not yet investing in ML. They are far more likely to:

  • Develop a single, integrated view of their target customer.
  • Have the ability to drill down to see underlying KPI data.
  • Check their KPI reports frequently.

These differences all depend on treating data as a valuable corporate asset. We see a strong correlation between companies that embrace ML and data-driven decision-making.

Augmenting Execution With Machine Learning

Nearly three quarters of survey respondents believe their organization’s current functional KPIs would be better achieved with greater investment in automation and machine-learning technologies. Our interviews with senior executives identified a variety of innovative ML practices. Without exception, the companies with the most intriguing and ambitious ML initiatives were the ones with the most serious commitment ― cultural and organizational ― to managing data as a valuable corporate asset.

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