The Decision Intelligence Handbook

Preface

The past few decades have brought astonishing improvements in data science, business intelligence, and artificial intelligence (AI). In response, organizations are increasingly determined to weave these technologies into the fabric of their everyday decisions. Indeed, a 2019 survey conducted by global consulting firm McKinsey found that better decision making can benefit a typical Fortune 500 company by as much as $250 million per year.

This reflects a big opportunity to improve organizational outcomes, even as it reflects the dismal state of organizational decision making today.

But we’re far from achieving this nirvana. In Fortune magazine, Alan Murray and Jackson Fordyce write, “Business leaders are so overwhelmed with data they’re struggling to function.” And today, many “data-driven” and “evidence-based” initiatives are falling short. The reason is, simply, that decision making is not really about data: it’s about achieving an organization’s outcomes, with data as a key ingredient, but still secondary to business outcomes. This incorrect focus on the data itself leads to data and AI work that isn’t well aligned with many organizations’ outcomes and desired goals.

Smart organizations are moving, instead, to “outcome-driven” decision making, with data and technology working “under the hood” to supercharge their choices.

Along the way, a new discipline has emerged to help them, called decision intelligence (DI). DI brings AI (including generative AI technologies like ...