by Arun Ravindran, Aparna Kapoor, Piyush Mishra and John Gomez

AI adoption and data maturity within enterprises have seen significant growth in the past decade. With each passing day, new enterprise AI use cases come to life in more organizations and more industries. Some enterprises, especially those at the more mature end of this spectrum, have made tremendous progress productizing their data science capabilities, and have set up large data and model capabilities to fuel their customer growth.

Many of these same companies are looking for ways to make more data accessible to their AI programs and increase business growth…

By Arun Ravindran, Hamid Maher, and Rata Jacquemart (alumnus)

Agriculture is at the forefront of climate change. As temperatures, humidity, and rainfall patterns shift, agricultural businesses around the globe — from family farms to multinational enterprises — will be tremendously affected, whether through soil chemistry, insect migration, or other factors threatening crop quality and yields. (See Exhibit 1.)

Authors: Sylvain Duranton and Steven Mills

BCG is deeply committed to its role as a leader in Responsible AI. But this isn’t just talk. The guidance we provide to our clients reflects the Responsible AI principles and practices BCG adheres to internally.

BCG is in the business of helping companies solve their most challenging business problems. As we work with companies to find solutions, which increasingly involve artificial intelligence, we adhere to a clear set of values that are core to who we are as a company: delivering solutions with integrity, respecting individuals, and always being mindful of the social…

by Sebastian Bak, Mark Abraham, Nicolas De Bellefonds, Aaron Arnoldsen

As the COVID-19 pandemic has brought into high relief, traditional demand forecasting is, for all intents and purposes, dead. Rather than look to the past, companies must reimagine their budgeting and funding capabilities and improve their ability to predict change in an increasingly unpredictable world. Rather than rely on historical data, companies must move towards leveraging high-frequency data and AI solutions to better anticipate demand and adjust their commercial, operational and corporate plans accordingly.

Upgrading demand forecasting capabilities is no longer a COVID-19 specific need, but rather a critical lever…

by Allen Chen, Andrew Mendoza, Gael Varoquaux, Steven Mills and Vladimir Lukic

When AI was first introduced into business processes, it was transformative, enabling companies to leverage the vast amounts of accumulated data to improve planning and decision making. It soon became apparent, however, that integrating AI into business processes at scale required significant resources. First, companies had to recruit highly sought-after (and highly paid) data scientists to create the data models behind AI. Second, the process of building and training the machine learning models that accelerated the data analysis process required a significant expenditure of time and energy. …

By Steven Mills and Cathy Carlisi

This is the second in a series of articles exploring how companies can become leaders in Responsible AI. The focus of the first article was on defining Responsible AI. In this article we share a perspective on why business leaders should care.

When developing AI systems, companies do not need to choose between protecting customers and growing the bottom line. This is a false choice. …

by Anni Coden

Unless you have a personal connection to them, you may never have heard of alpha-1 antitrypsin, eosinophilic esophagitis, or Barth syndrome. These are just three of an estimated 7,000 rare diseases that affect about 1 in 10 people in the U.S. Fifty percent of these rare diseases affect children — 30% of whom will die from them before the age of 5.

Those researching other, more prevalent diseases such as breast cancer and multiple sclerosis have a wealth of data to mine for possible treatments. Precisely because these other diseases are rare, researchers are hard-pressed to find…

Delivering transformative analytics isn’t about being better. It’s about doing things differently.

by Steven Mills and Vladimir Lukic

Over the past year, our data science team has delivered nearly 500 analytic use cases to a variety of private and public sector organizations, including some of the largest, most successful companies in the world. We have even found ourselves tackling problems their internal teams have already tried unsuccessfully to solve. The depth and breadth of this experience has given us a clear understanding of what it takes to successfully deliver use cases — and it may not be what you expect!

Our typical engagement begins with an intense, 1 to 2-month sprint where…

by Philipp Gerbert

Digital technology has shrunk the planet and enabled effective globalization — at least from a communication and control point of view. But has it? As intelligent machines proliferate, their much faster decisions and actions across the planet hit the physical limits of synchronization throughout space and time. The coordination challenge is reminiscent of the initial global stretch in the age of Christopher Columbus and Vasco da Gama, when the capitals of the Iberian empires could receive information and impose their will only with a significant delay. It creates novel challenges for business and for society at large…

BCG GAMMA editor

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