Is AI Data Driven, Algorithm Driven, or Process Driven?

There are those that think that data is the oil of AI and the focus should be clean data, data science and deep understanding of what the data means. There are those that say data is meaningless without context that can be with other data, models/algorithms or processes. Let’s explore the arguments in a concise fashion to discover the advantage of each view.

The Case for Data Driven:

Data is the starting point as it is a very useful asset. True or not, it is assumed that data carries knowledge and that tapping that knowledge will give advantage to those that study data well. It just makes sense for AI to start with data and leverage an advantage can be had by learning from it. This is especially true in the age of big and fast data. It seems especially true when sensing signals and patterns that are occurring in emergent situations. Businesses have had a long history with business intelligence and a great deal of the effort surrounds data. Why would it be any different with AI?

The Case for Algorithm Driven:

Understanding the advantage that algorithms have over static data in the wild is important. In fact, organizations can gain the upper hand by having an algorithm that optimizes their business. In fact, finding the right formula, statistical model or projection that is appropriate for the situation is the real art of business. These algorithms are guarded by organizations and are often considered the secret sauce for success. While they are dependent on clean data, the rules implied in the math or the logic are the real differentiators for many industries. Where would the insurance industry be without actuaries and their prized algorithms? AI will be no different.

The Case for Process Driven:

The importance of sequence and president is crucial in doing the right steps or tasks in the proper order to obtain results. It makes no difference, if the process is static and repeatable or dynamic and emergent. Knowing the next best action is the key to getting the best business results. Bringing to bear the right data and algorithms at the right time is what process is all about. With the precision of process, business outcomes are sure to be on point and appropriate adjustments can be made with a transparent feedback cycle that employs various forms of monitoring.

Net; Net:

The real story here is that you need all three for long term success. You may start with one and add the other. AI is truly starting with data this time as machine learning ramps up to its power curve.  As AI progresses, it will have to cooperate with both algorithms and processes. Data based AI is working well today and it will likely lead to rule based AI again as the sophistication and scope of the problems expand.  A triangle needs all three sides.