To Start an Artificial Intelligence Strategy, Follow the Data

February 4, 2016 Kris Hammond

Artificial Intelligence Strategy image

Cognitive computing. Machine intelligence. Smart machines. These are just a few of the phrases among dozens that were created as rebranding efforts to cut down on the fear and hesitation that artificial intelligence has inspired in the past. Regardless, AI is back and here to stay.

Recommendation systems are everywhere. Companies are clamoring for predictive and prescriptive analytics engines. And a day doesn’t go by without news of another learning or advanced reasoning system that is executing on a task better than humans.

The emergence of these intelligent systems and the ever-increasing hype around them has led to companies trying to figure out their own “AI strategy.” The AI category is vast and broad, there are many solutions to solve many problems but all these options create a problem - companies are challenged to understand what solutions truly align with their unique needs. Companies know they need to adopt intelligent systems or else be left to stagnate but a lack of understandable value propositions or well-articulated explanations of the technology can leave executives scratching their heads when faced with deciding on a strategy.

If AI is to be applied properly in the enterprise, this blind adoption strategy will not serve us well. No one should purchase software on the basis of hype and fear - you are destined to fail before the full potential and return on investment can even be explored.

So, why are technologies that faltered in the past working well today? What happened? It was not a change in the technology but rather a change in the environment. Primarily, a change in the data.

The intelligent systems succeeding today are built on a foundation of massive data sets.  Google’s Deep Learning, IBM Watson, and the assorted recommendation systems telling us what books and movies we should read are driven by huge data sets. It is the data of our work and world.  It is the data that is directly related to the tasks that these systems are trying to address. And even more important, it is the data tied to these processes that deserve a closer look at a potential area for automation.

To read the full article, visit DataInformed

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