Most companies are struggling to realize artificial intelligence's potential to completely transform the way they do business. The problem is, they typically apply AI in a long list of discrete uses, an approach that doesn't produce consequential change. Yet trying to overhaul the whole organization with AI all at once is simply too complicated to be practical. What's the solution? Using AI to reimagine one entire core business process, journey, or function end to end, say three McKinsey consultants. That allows each AI effort to build off the previous one by, say, reusing data or enhancing capabilities for a common set of stakeholders. An airline, for example, focused on its cargo function, and a telecom provider on its process for managing customer value. Scaling up AI involves four steps: (1) Identify an area where AI will make a big difference reasonably quickly and there are multiple interconnected activities and opportunities to share technology. (2) Staff the team with the right people and remove the obstacles to their success. (3) Reimagine business as usual, working back from a key goal and then exploring in detail how to achieve it. (4) Support new AI-based processes with organizational changes, such as interdisciplinary collaboration and agile mindsets.

Artificial intelligence seems to be on the brink of a boom. It's now guiding decisions on everything from crop harvests to bank loans, and uses like totally automated customer service are on the horizon. Indeed, McKinsey estimates that AI will add $13 trillion to the global economy in the next decade. Yet companies are struggling to scale up their AI efforts. Most have run only ad hoc projects or applied AI in just a single business process. In surveys of thousands of executives and work with hundreds of clients, McKinsey has identified how firms can capture the full AI opportunity. The key is to understand the organizational and cultural barriers AI initiatives face and work to lower them. That means shifting workers away from traditional mindsets, like relying on top-down decision making, which often run counter to those needed for AI. Leaders can also set up AI projects for success by conveying their urgency and benefits; investing heavily in AI education and adoption; and accounting for the company's AI maturity, business complexity, and innovation pace when deciding how work should be organized.