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Scott Schlesinger, US data and analytics lead at PA Consulting, and Scott Siegel, data and analytics expert at PA, explain how manufacturers can utilize predictive asset maintenance to reduce supply chain impacts.

Artificial intelligence (AI) and machine learning (ML) are some of the most hyped enterprise technologies and have caught the imagination of boards, with the promise of efficiencies and lower costs, and the public, with developments such as self-driving cars and autonomous quadcopter air taxis. Of course, the reality is rather more prosaic, with firms looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Organisations are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and analysing business performance for loans, demand prediction for seasonal products and crunching through vast amounts of data to optimise energy grids. All this falls short of the idea of AI as an intelligent machine along the lines of 2001: A Space Odyssey's HAL. But it is still a fast-growing market, driven by businesses trying to drive more value from their data, and automate business intelligence and analytics to improve decision-making. Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62bn this year, with the fastest growth coming from knowledge management.

You would hope intelligent human beings would be able to agree on core moral standpoints for technologies, and for some this certainly seems achievable. Discussing the ethics of AI, though, is far more complex than simply agreeing not to fly your drone into a person. To work within the real world, AI has to be aware of the nuances and particulars of specific societies. An AI system in a high surveillance country might differ from its equivalents in other parts of the world. Then, of course, there is ethical divide within societies.