Artificial Intelligence (AI) is top of mind for leading corporations these days – 96.4% of top executives reported earlier this year that AI was the number one disruptive technology that they were investing in, up from 68.9% just two years ago. In addition, 80% of these executives identified AI as the most impactful disruptive technology, up from 46.6% two years earlier. Yet, for many organizations, Artificial Intelligence remains a mystery. For specialists, AI implies a very specific connotation in terms of intelligence demonstrated by machines, in contrast to the more common usage of AI which encompasses all varieties of machine assisted learning, most notably machine learning, deep learning, and natural language. For the sake of this discussion, we will assume the broadest definition of AI.

Opportunity / Framework Generation defining technology – AI will ultimately have an impact on productivity on the magnitude of steam power, electrification, computing, etc. Core tech done by others – the frameworks (e.g. Microsoft LUIS) will either be open source & collaborative or otherwise require immense amounts of capital and data to develop and therefore are better left to the Internet Giants and Silicon Valley-based investors. AI includes any tech that allows machines to simulate the cognitive capabilities of a human. However, consensus has changed over time; as AI technologies go from leading edge to commercially accepted to mundane, those technologies are often dismissed as "not real AI". For the purposes of this paper, we define AI to be the generation of technologies that have been enabled by advances in machine learning ("ML").

Since 2012, over 250 companies involved in artificial intelligence have been acquired. Over half of these have been in the last two years - with the vast majority engaged in some aspect of machine learning. Efficient independent learning, machine or human, employs a feedback loop to generate solutions and an evaluation of those solutions leading to better solutions the next time around. Control of any part of that feedback loop is a valuable resource to enable AI, that is, to make smarter systems and enable the systems to function better. These are the two sides of the AI Enablement trend.