Enterprises of all sizes and across virtually all markets are scrambling to augment their analytics capabilities with artificial intelligence (AI) in the hopes of gaining a competitive advantage in a challenging post-pandemic economy.

Plenty of anecdotal evidence points to AI’s ability to improve analytics, but there seems to be less conversation around how it should be implemented in production environments, let alone how organizations should view it strategically over the long term.

Start with a plan

AI may be the latest iteration of digital technology, but like its predecessors, it is not infallible. More often than not, success hinges on the deployment and integration into existing environments, not the technology itself. So before rushing headlong into the AI tsunami, enterprise executives might want to take a moment to consider how they plan to use it and to what end.

According to Content Rules founder and CEO Val Swisher, AI can be applied to analytics in three ways: as a descriptive tool, a predictive tool, and a prescriptive tool. Descriptive AI is used to describe something that has happened in the past, usually by grouping data into clusters to detect patterns and outliers. This allows enterprises to answer the question, “What happened?” Predictive AI takes descriptive results and attempts to apply them to the future, again using massive data mining and storing. This answers the question, “What could happen?” Prescriptive AI then takes all this data and resulting analytics to help guide the process to a desired outcome, answering the question “What should happen?”

Depending on your objectives, you’ll need to pepper your analytics with varying levels of these three flavors of AI. But how is this done and how can they be scaled to production levels quickly and efficiently without losing control?

In a recent article on eWeek, SparkBeyond U.S. data science head Ryan Grosso offered up a number of tips to help “bridge the gap between analytic aspirations and ability.” Heading the list is the need to develop in-house analytics talent (as in, human talent) capable of managing the data science tasks AI requires. As well, you’ll need to create hybrid teams with expertise in various domains to replace the often siloed hierarchies that take root in complex organizations. The key here is to train data scientists and business executives to speak a common language. Only then should you select and deploy the proper AI-driven analytics platform, preferably one that can be tailored to your needs rather than requiring changes to your processes or business model.

Reading is fundamental

But what, exactly, should AI do once it is infused into the analytics process? What specific functions should it perform? According to Decipher Zone’s Mahipal Nehra, one of its key capabilities is to read text, lots of text, and extract meaning from what is essentially unstructured data. This means it can provide insight not just on the raw numbers coming from connected devices and monitoring solutions but the equally valuable abundance of communication taking place between employees, customers, partners and other stakeholders. This, in turn, can lead to valuable insights into consumer experiences, brand recognition and the organization’s overall reputation. And understanding text is part of the roadmap to full speech recognition, which opens up whole new possibilities in areas like customer relations and self-help applications.

Even for AI, however, the more difficult it is to gather and parse all this data, the more costly and error-prone the analytics platform will be. This is why one of the key elements in any AI strategy is to get your data house in order, say Manveer Sahota and Chris D’Agostino of DataBricks. One way to do this is to combine legacy data warehouses and lakes under a unified management system that leverages the scale of the former and the flexibility of the latter. This enables the kind of fine-grained control and governance to maximize data availability to intelligent analytics tools without jeopardizing privacy and security.

It should also be noted that deploying AI into analytics is not just a one-and-done endeavor. Both the software deployment and the data it accesses will be in a constant state of flux, growing and evolving at the speed of modern business. Usually, the most valuable insights gleaned from AI will require you to change what you are doing and how you are doing it, and this can be difficult, particularly in large organizations. But after all the time, effort and expense of putting this intelligent analytics operation in place, it would be a shame to ignore what it has to say only to be out-performed by a more AI-savvy competitor.