AI for business users refers to features that use AI under the hood of a BI tool. For instance, BI vendors are enhancing their products with AI capabilities that make the tools easier to use and offer automated insights. In many cases, the business user will not know that AI algorithms are running in the background. For example, a BI tool might suggest an adequate visualization or automatically cleanse duplicate and flawed records in data sets. AI for data analysts refers to functions that enable data engineers and data scientists to prepare, create, and deploy analytic models within a BI tool. Rather than import AI models into a BI tool, this functionality lets data analysts build models inside BI tools where they can leverage the tools’ ability to extract data from multiple data sources, develop complex data flows that can daisy-chain multiple advanced algorithms, and deploy models within BI reports and dashboards or to external engines via an API.

The goal of artificial intelligence is to enrich machines with cognitive capabilities to create an intelligent agent that perceives its environment and takes actions that maximize the chance of success at some goal [5]. For this, AI encompasses various concepts from machine learning, natural language parsing and many other fields.

Since AI just started hitting the market in the past year or two, I’m not surprised it’s not a concrete defined entity and that there’s ambiguity around it. I describe it as a set of processes that directly affect an individual's or human being’s behavior. So, if you’re trying to interact with a product and the product does not have AI, it’s going to take a little bit of interrogation in order to get the product to do what you want. If the product has embedded AI, especially if the product was built with AI from the ground up, then your interaction with the product will be much more efficient and much more pleasing to the consumer.