With apologies to Peter Drucker, it is no longer simply what you measure that determines what you manage. It's how you discover what to measure that determines how you manage. In industry after industry, we see innovative measurement systems leading to innovative metrics and new organizational behaviors that drive superior outcomes. More organizations are recognizing that benchmarking and executive expertise don't always determine the best key performance indicators (KPIs). These data-driven companies employ predictive analytics such as machine learning, along with leadership acumen, to identify and refine key strategic measures.

AI now has a firm footing in organizations' strategic decision-making processes. Five years ago, less than 10% of large companies had adopted machine learning or other forms of AI, but today 80% of them make use of the technology.1 Whether it is Amazon integrating algorithms into its recruiting processes or Walmart using AI for decisions about product lines, such examples show that the use of AI now transcends mere process automation and that AI is increasingly being used to augment decision-making processes at all levels, including top management.2 In the boardroom, companies can use the power of AI to analyze information, recognize complex patterns, and even get advice on strategic issues. This predictive technology can help executives handle the increasing complexity of strategic choices by offering new perspectives and insights for consideration, which can help organizations gain competitive advantage.3 Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.

The graph represents a network of 4,360 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 26 April 2021 at 19:19 UTC. The requested start date was Monday, 26 April 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 3-hour, 39-minute period from Thursday, 22 April 2021 at 20:21 UTC to Monday, 26 April 2021 at 00:00 UTC.

This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1