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Yes, Artificial Intelligence is Analytics

There seems to be some confusion as to exactly what artificial intelligence (AI) is, and how the discipline of AI should be categorized. Is AI a form of analytics or is it a totally new discipline that is distinct from analytics? I firmly believe that AI is more closely related to predictive analytics and data science than to any other discipline. One might even argue that AI is the next generation of predictive analytics. Additionally, AI is often utilized in situations where it is necessary to operationalize the analytics process. So, in that sense, AI is also often pushing the envelope of prescriptive, operationalized analytics. It would be a mistake to say that AI is not a form of analytics.


Let’s review a few basic facts that help define predictive analytics and then look at how AI fits well within those bounds. At its core, predictive analytics is, naturally, about predicting something. Who will buy? Will certain equipment break? Which price will maximize profits? Each of these questions can be addressed by following a familiar workflow:

  • First, we identify a metric or state that we want to predict and gather historical information on that metric or state. For example, identifying which individuals among millions of customers responded to past marketing campaigns.

  • Next, we gather additional data that we believe could be relevant to predicting our target. For example, each customer’s past spending, demographic profile, and more.

  • Then, we pass the data through one or more algorithms that attempt to find a relationship between the target and the additional data.

  • Through this process, a model is created that produces a prediction if new data is fed to it. If a customer had this profile, how likely would she be to respond? If we priced at this point, how much profit might we expect?

The goals and steps followed within an AI process are the same. Let’s look at two examples.

Take image recognition. First, we identify a bunch of cat pictures. Then, we grab a bunch of non-cat pictures. We pass a deep learning algorithm over the images to learn to accurately predict whether or not an image is a cat. When provided with a new image, the model will answer with the probability that the image is a cat. Sounds a lot like predictive analytics, doesn’t it?

Let’s now consider natural language processing (NLP). We gather a wide range of statements that have specific meanings we care about. We also gather a wide range of other statements. We run NLP procedures against the data to try to tease out how to tell what is important and how to tell what is being asked. As we feed a new line of text to the process, it will identify what the point of the statement is in probabilistic terms. The NLP process will assign probabilities to various possible interpretations and send those back (think Watson playing jeopardy). This also sounds a lot like predictive analytics.


As I wrote about in The Analytics Revolution, a major trend today is to embed predictive analytics into business processes so that the models are utilized in an automated, embedded, prescriptive fashion at the point of a business decision. For example, as a person navigates a web page, models are utilized to predict what offers should appear on the next page. There is no human intervention once the process is in place. The process makes offers until told to stop.

Many applications of AI today also require industrialization. For example, as an image is posted on social media, it is immediately analyzed to identify who is present in the image. As I make a statement to Siri or Alexa, it attempts to determine what I said and what the best answer is. While this qualifies as a more advanced application of predictive analytics that moves into embedded, prescriptive, automated processes, it is still very much in line with how predictive analytics are being used today.


Look to your analytics and data science organization to drive AI for you. That is the team already familiar with wrangling data to make predictions, pushing those predictions out into business processes, and tracking the results. The mindset and underlying skill set required for AI are very much in line with those on the analytics and data science team. No other team is even close. Place responsibility with those most able to succeed.

Given the rising importance of AI, it must be included in your analytic strategy in order for that strategy to be credible and complete. Note that this does not mean that your strategy has to include deploying AI in the short term. You may have other things to get in order first before pursuing AI. However, even if AI is not yet a priority, that fact should at least be accounted for in your analytics strategy. Simply call out the fact that AI’s role was considered and that it was determined to focus elsewhere in the short term. Just as big data couldn’t be ignored a few years ago, AI can’t be ignored today.

Your journey to a successful AI program will be far easier if you recognize and embrace AI as a form of analytics and then task your analytics organization with leading the charge. Don’t cause confusion and redundancy by considering AI to be something completely different.