Less than 10% … that's how many artificial-intelligence test projects are estimated to be deployed into full-scale production in enterprise environments …

Less than 10% … that's how many artificial-intelligence test projects are estimated to be deployed into full-scale production in enterprise environments, according to a recent report from the International Institute for Analytics. There are a number of reasons for this surprisingly small amount, including an overwhelming amount of data and the lack of easy-to-use tools to analyze it. It's a problem that calls for operationalizing AI and machine learning, making it accessible and repeatable consistently. "Ultimately, if you want to get business value from those models and all of the hard work that you've done, it has to be injected into the business process," said Anant Chintamaneni (pictured), vice president and general manager of BlueData at Hewlett Packard Enterprise Co. "Operationalization of machine learning is ultimately the key, and that's the progression that enterprises have to make." Burris was joined for a digital community event by co-host Stu Miniman (@stu), and they also interviewed Nanda Vijaydev, distinguished technologist and lead data scientist at HPE; Patrick Osborne, vice president and general manager of big data, analytics, and scale-out data platforms at HPE; and Wikibon analyst James Kobielus (@jameskobielus).