It's 10 a.m. on a Monday, and Aman, one of the developers of a new artificial intelligence tool, is excited about the technology launching that day. Leaders of Duke University Hospital's intensive care unit had asked Aman and his colleagues to develop an AI tool to help prevent overcrowding in their unit. Research had shown that patients coming to the hospital with a particular type of heart attack did not require hospitalization in the ICU, and its leaders hoped that an AI tool would help emergency room clinicians identify these patients and refer them to noncritical care. This would both improve quality of care for patients and reduce unnecessary costs. Aman and his team of cardiologists, data scientists, computer scientists, and project managers had developed an AI tool that made it easy for clinicians to identify these patients.

For most of us, the way we live our day-to-day lives has changed substantially since the beginning of the pandemic, with many of those changes now permanent. This shift has introduced opportunities and some challenges for businesses of all shapes and sizes. It's not just Zoom and Netflix that are seeing the transformative effects. A recent study found that property and casualty insurance customers now have much higher expectations for their insurers' websites and mobile apps. Similarly, mainstream adoption of telehealth has continued to surge, growing by 36% in 2021 as health care consumers adopted it as a more routine option and states expanded insurance coverage for telemedicine.

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.

It's been more than 50 years since HAL, the malevolent computer in the movie 2001: A Space Odyssey, first terrified audiences by turning against the astronauts he was supposed to protect. That cinematic moment captures what many of us still fear in AI: that it may gain superhuman powers and subjugate us. But instead of worrying about futuristic sci-fi nightmares, we should instead wake up to an equally alarming scenario that is unfolding before our eyes: We are increasingly, unsuspectingly yet willingly, abdicating our power to make decisions based on our own judgment, including our moral convictions. What we believe is "right" risks becoming no longer a question of ethics but simply what the "correct" result of a mathematical calculation is. Day to day, computers already make many decisions for us, and on the surface, they seem to be doing a good job.

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.

In January 2020, an unknown American facial recognition software company, Clearview AI, was thrust into the limelight. It had quietly flown under the radar until The New York Times reported that businesses, law enforcement agencies, universities, and individuals had been purchasing its sophisticated facial recognition software, whose algorithm could match human faces to a database of over 3 billion images the company had collected from the internet. The article renewed the global debate about the use of AI-based facial recognition technology by governments and law enforcement agencies. Many people called for a ban on the use of the Clearview AI technology because the startup had created its database by mining social media websites and the internet for photographs but hadn't obtained permission to index individuals' faces. Twitter almost immediately sent the company a cease-and-delete letter, and YouTube and Facebook followed suit.

Companies embarking on AI and data science initiatives in the current economy should strive for a level of economic return higher than those achieved by many companies in the early days of enterprise AI. Several surveys suggest a low level of returns thus far, in part because many AI systems were never deployed: A 2021 IBM survey, for instance, found that only 21% of 5,501 companies said they had "deployed AI across the business," while the remainder said they are exploring AI, developing proofs of concept, or using pre-built AI applications. Similarly, a VentureBeat analysis suggests that 87% of AI models are never put into production. And a 2019 MIT Sloan Management Review/Boston Consulting Group survey found that 7 out of 10 companies reported no value from their AI investments. This makes sense: If there is no production deployment, there is no economic value.

Many business leaders are overlooking a way to grow their company and capture untapped value. Most of them understand the superior value of business models built around subscription-based software as a service (SaaS) and models built around marketplaces that join together many buyers and sellers. Few, however, understand the exponential growth and value that comes when both of those strategies are combined with data and machine learning models. As well, many leaders simply haven't considered adding these strategies and models to their own business to create platform economics and growth -- whether they're running a startup, a midsize company, or a legacy organization. But the opportunity to integrate these three strategic elements is becoming a critical business imperative.

Automation technologies can offer considerable value to organizations, but only if they are implemented strategically, on a large scale, and in support of business goals. They also require the businesses using them to actively monitor performance and make adjustments over time. Because of the complexity of the technologies and the need to think broadly about their capabilities, sophisticated users of automation -- whether financial services firms, data management companies, or hospitals -- are creating automation (or sometimes "intelligent automation") centers of excellence (COEs). A COE is a dedicated team of individuals who set standards, provide consulting and development assistance, and monitor the technology's progress. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.

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