Evolution of the Internet over several past decades into a global interconnected web has changed entirely the technology landscape, social circumstances and market conditions. The overall effect is acceleration, amplification and automation. New companies emerged, growing so big and fast that they now dominate certain global markets. All this would be difficult to imagine without a global infrastructure fabric containing millions of servers in strategically placed data centers and serving billions of customers daily. Established corporations have been late in transforming their IT infrastructure in a similar fashion, which is the simplest explanation as to why cloud computing is making major inroads into markets today.

Microsoft's purchase of Swiftkey for 250 million this month was preceded by Google's 582 million for DeepMind in 2014, a buy vindicated by its subsequent ability to master the ancient Chinese game of Go. In 2012, Amazon bought Cambridge-based Evi Technologies, creator of a Siri-like product that can field users' questions. But if you can't afford to buy an AI startup, algorithms are still within reach. Enterprises with the cash, data and wherewithal are kick-starting their own machine-learning efforts in a bid to gain competitive advantage. In a Computerworld article, John Dodge details the work of sector leaders that are data-rich and savvy enough to develop their own algorithms in-house.

McKinsey analysts recently asked how traditional industries are now using machine learning to gather fresh business insights. With processing power so cheap, chewing through petabytes of data in order for machine learning to happen is no longer an activity restricted to science boffins or the cash-rich. No surprise then that the McKinsey list documents impressive examples in America. "This past spring, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum, a California machine-learning start-up. By digitizing the past few seasons' games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, "a bad shooter who takes good shots and a good shooter who takes bad shots"--and to adjust his decisions accordingly".