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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The PEAbGLP is based on empiric busines (and non-business) cases and experiences, which can be described by using a Natural Language Programming (NLP) that can be integrated by using Enterprise Architecture (EA) concepts and design languages. An AR based LP supports an Entity wide Polymathic learning strategy. The PEAbGLP offers a set of recommendations and an adapted IHITF that includes AI/LP, EA, business engineering, managerial, and technical propositions. The PEAbGLP can be used by executive managers, ETP managers, business architects/analysts, and Intelligence implementation engineers to enable solutions to transform business, and LP based AI environments. The RDP uses a Polymathic approach and applies an interdisciplinary AR based LP. The AHMM4AI based PEABGLP, combines various EA, AI, Mathematical Modelling (MM), academic/educational, and ETP (simply Project) domains. These domains can be: Business engineering, IHITF, (Re)Organization concepts, Major/frequent changes, Financial engineering, AI, Project Management (PM), MM/algorithms, Information and Communication Systems (ICS), EA, geoeconomics/geopolitical analysis, and other. This article is linked to all the author’s works and their RDP findings (Trad, 2023a, 2023b). This RDP is not a simplistic quantitative, but a complex qualitative one that is based on Polymathics, which can appear as complex for the reader. Polymathic research is needed because the rate of failures in such Projects is about 95%.
Figure 1. The integration of the LP in the project
LP related topics/domains that includes: 1) An adapted evolutive RDP; 2) Enriches the IHITF, to avoid locked-in; 3) Uses a methodological approach that is based EA and AR/LP/AI; and 4) A Polymathic and Interdisciplinary Engineering Project Work (PIEPW) (Trad, 2023a, 2023b) to support Entity’s Knowledge Management System (KMS). The PIEPW is used to integrate LPs as shown in Figure 1. The PEABGLP use integrated: Critical Success Areas (CSA), Critical Success Factors (CSF), Key Performance Indicators (KPI), Concrete ICS variables (CSAs, CSFs, KPIs, and concrete ICS variables, or simply Factors), The LP uses the HDT to support the DMS. Projects need Polymathic profiles which are extremely rare and they intensive LP based coaching. The PEAbGLP manages acquired experiences from encountered problems. The PEABGLP links Project topics like in Architecture in civil engineering and help teams in modelling activities; and the Entity’s engineers have the needed LP implementation skills. The PEABGLP can be based on existing Project frameworks like: 1) EA environments to support LPs (Pushpakumara, Jayaweera, & Manjulan, 2021); 2) An IHITF and an MM like the AHMM4AI (Trad, & Kalpić, 2019a); 3) Unbundled Entity’s services pool (Trad, 2015a, 2023e); 4) EA and AI domains (Trad, & Kalpić, 2022a, 2022b); 5) A scalable ICS and an agile PIEPW (Trad, 2023d); 6) A PIEPW based PM method (Spencer, 2016); 7) A transformable PEABGLP (Trad, & Kalpić, 2021b); and 8) The PEABGLP problem solving is supported by the KMS (Blackburn, & Rosen, 1993). .
PEABGLP’s Characteristics
Figure 2.
The interaction between the project and the RDP
Polymathic skills and characteristics for Projects are developed and improved along the Project. The PEABGLP needs an LP based DMS/KMS in order to solve all Project’s types of problems and to optimize PMs’ schedules. This chapter uses an IHITF, which is the author’s Transformation Research Architecture Development framework (TRADf) that includes: 1) ICS, PIEPW, EAIC, and corresponding LP patterns; 2) The DMS/KMS; 3) PEABGLP generators; and 5) An RDP, which is the 1st CSA and its heading are in fact the initial set of CSFs.