Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption. "We're very early in this trend," says Chui. "As much as we're seeing this growth in adoption, less than a third of the companies that we surveyed have deployed AI in multiple businesses or functions."

AI works great in the labs, but is it ready for prime time? Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption.

AI works great in the labs, but is it ready for prime time? Is artificial intelligence ready to move out of the labs and proofs of concept and into the mainstreams of enterprises? Leading industry observers say yes, it's ready -- but organizations may still need some preparation. "We've moved beyond the phase of'Is AI a shiny object?' toward broader mainstream adoption and actual value creation," says Bryce Hall, associate partner with McKinsey, noting that data from a survey of 2,300 executives shows a nearly 25% year-over-year increase in the use of AI in standard business processes. Hall joined McKinsey colleague Michael Chui in a recent podcast Q&A, in which they note that while AI is being widely adopted, much of the work is confined to specific narrow use cases, versus more strategic enterprise-centric adoption.

The wave of technological changes is fast sweeping across multiple fronts. The rapid adoption of transformative technologies such as machine learning and artificial intelligence is putting us on the cusp of a new digital age. While we keep adding every'digital progress story' to the list of the'next big thing', another technological advancement in the physical space also has immense potential to disrupt the status quo. Robotics is indeed realigning the rich human talent pool, altering ways in which we operate. One of the most prominent examples is robotics in manufacturing.

Heino Gevers, Customer Experience Director at Mimecast MEA, explains customer experience is the way a company tracks and organises every interaction between a customer, and the organisation throughout the customer lifecycle. Customer experience is becoming a competitive advantage as the business-consumer relationship shifts, Nicholas O'Connor, Strategic Business Development Consultant at hi.guru, adds. CX, as it is often termed, makes use of data to drive strategic imperatives and build loyalty, he says. Certified Customer Experience Professional and consultant Julia Ahlfeldt adds it costs six to seven times more to acquire a new customer than it costs to keep an existing one. "If companies choose not to embrace CX, they run the risk of delivering negative experiences, which erode loyalty. Over time, these companies will gradually lose favor with customers, and their market share will diminish as consumers vote with their feet."

Every Monday morning, 75 million Spotify users logon to their devices and play a "Discover Weekly" playlist that's been curated to their musical preferences. Unbeknownst to many, the custom playlist they're enjoying was curated by a machine learning algorithm that has learned their unique musical preferences based on previous interactions with songs, musicians, and playlists. Machine learning, and a more advanced technology called deep learning, are types of artificial intelligence that allow a computer to learn information based on the data it is given. To borrow the words of Drew Breunig, "In a nutshell, deep learning is human recognition at computer scale." Essentially, the more information the computer is given, the better it can learn -- and in the case of platforms like Spotify or Netflix, the more interaction you have with the program, the better it can recommend music, movies, or TV shows that you'll like.