Attention is focused mental engagement on a particular item of business. Items come into our awareness, we attend to a particular item, and then we d… - Thomas H. Davenport

Some organizations are attempting to predict the nature of future jobs and the skills they will require. This is, of course, difficult or impossible to do with any precision.

The businesses and organizations that succeed with AI will be those that invest steadily, rise above the hype, make a good match between their business problems and the capabilities of AI, and take the long view.

Every decade or so, the business world invents another term for how it extracts managerial and decision-making value from computerized data. In the 1970s the favored term was “decision support systems,” accurately reflecting the importance of a decision-centered approach to data analysis. In the early 80s, “executive information systems” was the preferred nomenclature, which addressed the use of these systems by senior managers. Later in that decade, emphasis shifted to the more technical-sounding “online analytical processing,” or OLAP. The 90s saw the rise of “business intelligence” as a descriptor. In the middle of 2000’s first decade, “analytics” began to come into favor, at least for the more statistical and mathematical forms of data analysis.