Abstract
This chapter proposes how to implement in-house polymathic enterprise architecture-based generic learning processes (PEAbGLP) that can be the fundament of a generic and transcendent enterprise's artificial intelligence (AI) concept (EAIC). Generic and transcendent means that it supports and interfaces with all AI and technology domains, like machine learning (ML), deep learning (DL), data sciences (DS), and others (simply intelligence). The EAIC uses the author's polymathic transformation framework that is specialized in enterprise transformation projects (ETP). ETPs have an extremely high level of failure rates, and added to this fact, AI products force siloed integration approaches, which are risky undertakings. The EAIC ensures business sustainability and operational excellence for the enterprise (simply entity), and the main problem is the adoption of a holistic and polymathic learning process (LP). The PEAbGLP presents how an entity can integrate intelligence, which can be supported by the author's (already mature) applied holistic mathematical model (AHMM) for LP-based AI.
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