There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value. As AI becomes pervasive, its impact - on the bottom line and society - must be the focus of organizations that use it.
There is an increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology
Older AI technologies like natural language processing, and newer ones like deep learning. . . full article on ForbesREAD FULL ARTICLE
Just as reengineering business processes was necessary to get value from technology in the 80s and 90s, so too is necessary for AI. Organizations must define the division of labor between human workers and smart machines.
A data-driven culture is a key enabler for effective AI. It builds motivation and knowledge, driving not only capability development, but user adoption of new systems.
The greatest impediment to effective algorithms is insufficient, poor quality, or unlabeled data. Data in AI drives competitive advantage, ethics, and model quality.
A lack of rigor around investment in AI can be just as damaging as a lack of investment. Quantification of impact - whether financial impact or on user behavior - enables the necessary adjustments for success.
Having overcome these challenges ourselves, we developed the ROAI framework: a standardized approach to assessing and rethinking AI investments. Our goal is to maximize impact on the human equation.SEE THE ROAI FRAMEWORK