Companies are scattered when queried about their definition of artificial intelligence and their stance on where to take it. We first got a glimpse of neural networks in the late 80s. Then we found ourselves questioning machine's ability to do work. Hollywood's utopian/dystopian visions created a number of opinions for us; but in the end, we failed from a lack of processing horsepower to really embrace the AI that were in the imaginations of people and businesses. While we had some of the math, we were doing it long-hand and it took forever to get anything accomplished.
With the advent of technologies from NVidia and the work of Deep Learning, new questions became possible and we ultimately look at some of the options that the movies have shown us. By itself, the new term Machine Learning is useful to a degree, but the real need is the ability to drive decisions that create behaviors we seek. Some of these goal behaviors are the original ones: upsell; cross-sell; and driving customer brand loyalty. We believe there are other decisions which exist that can leverage these new capabilities bringing them to the front line of business processes that drive the organization regardless of mission:
With all the possibilities, it is important that we understand that not a single machine learning process is paramount, but it is instead an ensemble of processes that allow us to discern and make resolutions to arrive at the outcomes we seek.Through intensive work, we need to identify critical features and how they are engineered into workable predictors. Within those predictors, we must examine what machine learning algorithms we require, and precisely what needs are answered. From there, we can continue the chain of learnings until we conclude with a result to the question was asked.
Within these four key areas lies many different approaches and ultimately the art of machine learning is putting together the various types to construct a single artificial intelligence.
Within the continuum of BPM, if we can leverage these machine learning capabilities and further explore how we can reduce work effort or more effectively bring the desire of our customer closer into the alignment of our business objectives. We need to recognize that inevitably; BPM will see a new disruption from the inclusion of machine learning.
From this lens, we believe that bringing this together in three parts is critical:
Leveraging the collaboration within a safe-harbor for a construct that allows our customer's brightest minds to interact with our practice to create new innovative transformations. This gives us key insights into new ways of achieving better business outcomes.
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