Prentiss Donohue, senior vice president, professional services, OpenText outlines in Information Age the seven key steps to help AI and machine learning deliver on its full potential. Artificial intelligence (AI) and machine learning (ML) are shifting from being business buzzwords toward wider enterprise adoption. The efforts around strategies and adoption are reminiscent of the cycle and tipping point for enterprise cloud strategies four years ago when companies no longer had the option to move to the cloud and it only became a question of when? And how? AI and ML strategies are in the same evolution mode as companies build their approaches. Below are some thoughts around the how.

If you've stumbled upon this article, you may already be in this position. However, what's more likely is that this is going to become your situation in near future, and learning from someone else's experience is now needed to prepare. While there's a plethora of theory around business applications for data analytics; there is a significant lack of practical, real-life experience to draw on. This is largely due to the fact that adoption of these technologies, for many industries, is new and the results of pilots are just coming to light now. Machine learning technologies are successfully used in predictive and recommendation services.