Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it’s incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That’s the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. “We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials,” state the report’s authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT.
For starters, data – the fuel that propels AI decision-making – is not ready for the leap. Most successful AI initiatives to date are based on machine learning (ML) systems, which depend on large data sets. There are many areas where AI and related technologies show great promise for “enabling innovations in design, measurement, and materials—creating new products and new methods of production,” Autor and his co-authors point out. “Still, our interviews find many companies consider themselves at the early stages of adoption of these techniques, figuring out how to collect and structure data such that they can apply greater insights to their existing operations. Doing so requires integrating multiple data sources, often for hundreds to thousands of machines in larger companies. It requires merging expertise from both operations and information technology, while ensuring continuous improvement in the production system.”
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