Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers. Yet I know that many businesses still need people to, e.g., read PDF documents about an office building and write down the sizes of the leasable units contained therein.

Anyone who has been following the Robotic Process Automation (RPA) revolution that is transforming enterprises worldwide has also been hearing about how artificial intelligence (AI) can augment traditional RPA tools to do more than just RPA alone can achieve. You might even have noticed that some RPA software vendors -- Automation Anywhere is one of them -- are attempting to be more precise with their language. Rather than call our intelligent software robot (bot) product an AI-based solution, we say it is built around cognitive computing theories. But is that any clearer? Let's try and dispel some of it.

Point One: the painful truth is that content is different from data. There are lots of varieties and forms. It represents over 60% of the rising tide of information coming into our organizations, and it doesn't easily lend itself to storage in the rows and columns of a database. Point Two: capitalizing on this opportunity - turning "content" from a "risk" that needs to be "controlled" to a source of new value and insight - requires that the focus of AI shift to the business context in which these tools are used. Generic AI engines are truly impressive in what they can do, but for organizations to move to the next level, these engines need to be geared to specific business processes.