Recent advancements in Artificial Intelligence fall under the umbrella of industrial facets of AI (Industrial AI, for short) and explainable AI (XAI). We advocate that in the realization of these two pursuits, information granules and Granular Computing play a significant role. First, it is shown that information granularity is of paramount relevance in building linkages between real-world data and symbols commonly encountered in AI processing. Second, we stress that a suitable level of abstraction (information granularity) becomes essential to support user-oriented framework of design and functioning AI artifacts. In both cases, central to all pursuits is a process of formation of information granules and their prudent characterization. We discuss a comprehensive approach to the development of information granules by means of the principle of justifiable granularity; here various construction scenarios are discussed. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular models. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data.