According to new research from the Infosys Knowledge Institute, companies can generate over $460 billion in incremental profit if they do three things: improve data practices, trust in advanced AI, and integrate AI with business operations. However, despite high expectations for data and artificial intelligence (AI), most companies fail to act on these areas to convert data science to business value. Infosys Data AI Radar: Making AI Real found that although three of four companies want to operate AI across their firms, most businesses are new to AI and face daunting challenges to scale. The report also found that 63% of AI models function only at basic capability, are driven by humans, and often fall short on data verification, data practices, and data strategies. Only 26% of practitioners are highly satisfied with their data and AI tools.

Register for the free webcast "Bringing Artificial Intelligence to the Enterprise," happening October 31, 2017, to learn more about the current state of AI within large enterprises. One of the paradoxes of artificial intelligence is that the companies poised to make the most of it are those with the most data: enterprises; yet, they have the most institutional and organizational difficulties to overcome to do so. In this episode of the O'Reilly podcast, I had a chance to discuss the challenges with someone whose job it is to tackle these issues--Ron Bodkin, VP and general manager of artificial intelligence at Teradata. Connected AI is an approach to deliver strategic value from AI by connecting it with high-quality data that's already curated and integrated in the enterprise. This typically starts with high-value transactional data, such as information about past purchases, customers, and accounts, as well as data about devices and configurations.