AI is not IT- and adopting artificial intelligence is almost nothing like adopting traditional software solutions. While software is deterministic, AI is probabilistic. The process of coaxing value from data with algorithms is a challenging and often time-consuming one. While non-technical AI project leaders and executives don't need to know how to clean data, write Python, or adjust for algorithmic drift – but they do have to understand the experimental process that subject-matter experts and data scientists go through to find value in data. Last week we covered the three phases of AI deployment, and this week we'll dive deeper in the seven steps of the data science lifecycle itself – and the aspects of the process that non-technical project leaders should understand.

This is part 2 of a three-part series examining the effects of robots and automation on employment, based on new research from economist and Institute Professor Daron Acemoglu. Overall, adding robots to manufacturing reduces jobs -- by more than three per robot, in fact. But a new study co-authored by an MIT professor reveals an important pattern: Firms that move quickly to use robots tend to add workers to their payroll, while industry job losses are more concentrated in firms that make this change more slowly. The study, by MIT economist Daron Acemoglu, examines the introduction of robots to French manufacturing in recent decades, illuminating the business dynamics and labor implications in granular detail. "When you look at use of robots at the firm level, it is really interesting because there is an additional dimension," says Acemoglu.

As significant disruptions across Industrial Internet of Things (IIoT), big data and predictive analytics continue to capture the attention of manufacturers, an EY study titled "'Will the next transformation in manufacturing be led by digital?" found that 66% of manufacturing firms in India ranked big data and predictive analytics as the top investment priority in technology in the next 1-2 years. While 63% of the organizations ranked sensors and Industrial Internet of Things (IIoT) as the second key priority, as many as 33% ranked cloud/integrated platforms along with robotic process automation as the third key priority for investment for transforming their current manufacturing process. The report incorporates first-hand perspectives of major manufacturing firms in India on the recent technological advancements and their adoption, as part of a survey conducted by EY. Ashish Nanda, EY India Supply Chain Leader said, "Concepts such as Industry 4.0 and Smart Factory, which interconnect the shop-floor ecosystem through emerging technologies, are now a reality. Digitization continues to transform manufacturing processes around the world leveraging technologies such as IIoT, artificial intelligence, advanced robotics, etc. However, the adoption of digital technologies in India is still in its infancy, considering that manufacturers have started using these technological advancements recently and with limited scope. Going by the success stories though, it is perhaps essential for manufacturing organisations in India to first understand and then embark on this digital transformational journey to remain competitive and attain world-class status."

As significant disruptions across Industrial Internet of Things (IIoT), big data and predictive analytics continue to capture the attention of manufacturers, an EY study titled "'Will the next transformation in manufacturing be led by digital?" found that 66% of manufacturing firms in India ranked big data and predictive analytics as the top investment priority in technology in the next 1-2 years. While 63% of the organizations ranked sensors and Industrial Internet of Things (IIoT) as the second key priority, as many as 33% ranked cloud/integrated platforms along with robotic process automation as the third key priority for investment for transforming their current manufacturing process. The report incorporates first-hand perspectives of major manufacturing firms in India on the recent technological advancements and their adoption, as part of a survey conducted by EY. Ashish Nanda, EY India Supply Chain Leader said, "Concepts such as Industry 4.0 and Smart Factory, which interconnect the shop-floor ecosystem through emerging technologies, are now a reality. Digitization continues to transform manufacturing processes around the world leveraging technologies such as IIoT, artificial intelligence, advanced robotics, etc. However, the adoption of digital technologies in India is still in its infancy, considering that manufacturers have started using these technological advancements recently and with limited scope. Going by the success stories though, it is perhaps essential for manufacturing organisations in India to first understand and then embark on this digital transformational journey to remain competitive and attain world-class status."

Many companies add AI to processes by building or buying single-task bots, such as NLP systems or vision recognition tools, and adding them to processes using traditional, non-AI methods. But human intelligence is still needed to tease out processes, disparate systems into a single coherent process, to change processes as the business evolves, and to spot and fix problems. According to McKinsey, AI, machine learning, and related technologies are now making inroads into this territory via robotic process automation (RPA). This combination of AI and RPA adds up to intelligent process automation (IPA). In addition to RPA and machine learning algorithms, IPA also includes process management software, natural language processing and generation, and cognitive agents, or "bots."

While the world watches anxiously for signs of US President Donald Trump's next move vis-a-vis China, Chinese leaders remain focused on the next stage of their country's ongoing economic transformation. What they do should interest everyone ― especially US policymakers. China's industrialization process, like that of other successful East Asian economies, has combined profit-led investment, active industrial policy, and export discipline. But that approach has its limits, exemplified in the numerous developing countries that have attempted to climb the same development ladder, only to become stuck on the middle rungs or even to fall back, owing to what Harvard University economist Dani Rodrik has called "premature deindustrialization." China hopes to avoid this fate, with the help of "China Manufacturing 2025" (CM2025), a roadmap released by Premier Li Keqiang in 2015 to guide the country's industrial modernization.

Arithmetically, the problem is a combination of collapsing productivity and insufficient capital investment. On February 19, 2017, the New York Times ran a feature story on recent changes in the United States oil industry.2 The focus was on the recent "embrace" of technological innovation in the industry after the 2014 plunge in the global oil market. This was just one of a rash of such pieces in the popular press, relying, as is typical of such writing, on a smattering of skewed, decontextualized data, a healthy serving of the anecdotal, and a host of the worst tech journalism clichés ("a few icons on a computer screen," "a click of the mouse," video game marathons as job training, a compulsory reference to drones). Zeroing in on the effects of these changes on workers in west Texas, the article's upshot is unobjectionable enough: as oil prices recover, output rises, and production becomes more capital-intensive, many workers who lost jobs in the downturn will be replaced by machines. These workers, often Latino, are sure to be forced out of these semi-skilled, relatively well-paid jobs into other sectors of the labor market, where their skills and experience will serve little purpose. At first blush, the situation seems dire. We are told that some 30% of jobs in the industry were lost after the oil market crash of mid-2014, when employment in the industry was at its peak.