One of Microsoft's very few failures over the years was the Microsoft Zune, an MP3 player that crashed and burned – largely because, by the time it was released, Apple had already cornered the market with its fruit-emblazoned counterpart. This is just one example showing how time-to-market is critical. The crucial nature of time-to-market is not a new concept but one that has driven global trade for thousands of years. Thirty years ago, a classic McKinsey study showed that companies lost 33% of after-tax profit as a consequence of products shipping six months late. Many factors feed into time-to-market, such as distribution, marketing strategy, production processes, and supply chains.

Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies' abilities to harness them.

AI is not just for driverless cars, digital assistants, or movie recommendations anymore; in multiple industries, it's a wave about to break. According to a recent McKinsey Global Institute study surveying 3,000 "AI-aware" companies around the globe, only 20 percent are using AI-related technologies in a core part of the business, but the majority expect to ramp up AI spending in the next three years. Other studies yield similar results. In an Infosys-funded survey of 1,600 business and IT leaders in seven countries, although only 25 percent said AI technologies were fully deployed and working within their organizations, 76 percent overall called AI critical to their companies' success. Organizations with partially or fully deployed AI technologies expect them to contribute a 39 percent increase in revenue and a 37 percent reduction in costs by 2020. Businesses on average have been using AI for about two years and expect mature adoption in three more years.