Why are those leading adopters so far ahead -- and what can others learn from them? MIT Machine Intelligence for Manufacturing and Operations (MIMO) and McKinsey and Company have the answer, revealed in a first-of-its-kind Harvard Business Review article. The piece chronicles how MIMO and McKinsey partnered for a sweeping 100-company survey to explain how high-performing companies successfully wield machine learning technologies (and where others could improve). Created by the MIT Leaders for Global Operations (LGO) program, MIMO is a research and educational program designed to boost industrial competitiveness by accelerating machine intelligence's deployment and understanding. The goal is to "find the shortest path from data to impact," says managing director Bruce Lawler SM '92.

Six-thousand retrenched National Australia Bank (NAB) employees start leaving from this week, largely from the bank's Melbourne head office, as software takes over increasingly complex tasks. The cuts -- one in every five members of NAB's workforce -- were announced in November, the same day the bank revealed a $5.3 billion annual net profit. Dominic Barton works for the world's top CEOs, as global managing partner of consulting firm McKinsey and Company. "For 60 per cent of jobs, 30 per cent of the activities are automatable," he said. In his view, automation and software that analyses information and makes decisions will transform the business landscape -- doing jobs that, until recently, required well-paid "knowledge workers".

Machine learning can help retailers address the challenges of offering fresh foods, which account for up to 40% of a grocers' revenue and one-third of the cost of goods sold, according to a report by McKinsey and Company. The increasing demand of these products have led to new offerings, like exotic and hard-to-find items as well as "ultrafresh" items with a shelf life of no more than one or two days. Old processes can make it difficult to order the correct amount of food: order too much, and the food goes to waste; order too little, and you lose sales. Most traditional supply chain planning systems take a fixed, rule-based approach to forecasting and replenishment, but because local demand and conditions vary from day to day, planners have to manually enter different types of data into their replenishment systems. These manual processes are time consuming, error prone and reliant on individual planners' experience and instincts.