Business Transformation and Enterprise Architecture Projects: Machine Learning Integration for Projects (MLI4P)

Introduction

In this book chapter the author presents an Artificial Intelligence (AI) based generic concept for decision making that is based on Machine Learning; where the AHMM4P manages various types of algorithms. A transformation depends on the capacities of the decision-making system and the profile of the Business Transformation Manager (or simply the Manager) and his team; who are supported by a holistic framework (Trad & Kalpić, 2020a). The role of Machine Learning Integration for Projects (MLI4P) and the needed data and modules’ modelling techniques are essential for managing various type of algorithms in an AI based transformation project. This chapter and the author’s related research publications deal with Business Transformation Projects’ (or simply Project) complexity as well as the support for the Decision-Making System for Projects (DMS4P) and Enterprise Architecture Integration for Projects (EAI4P). The proposed framework promotes the Project’s technics to ensure success, by: 1) modelling artefacts; 2) implementing MLI4P components; 3) EAI4P support; 4) the use of a Generic Project Interface (GPI); and 5) complex algorithmics. The success of a Project depends on how an EAI4P and complex algorithmic modelling activities are synchronized (IMD, 2015).

Figure 1.

EAI4P cycles synchronize with Project resources

That is why the implementation of such Projects requires significant knowledge of EAI4P techniques. GPI handles MLI4P calls and its main mechanisms to support: 1) a generic data architecture; 2) implementation interfaces; and 3) data and modules modelling. GPI is a part of the Selection management, Architecture-modelling, Control-monitoring, Decision-making, Training management and Project management Framework (SmAmCmDmTmPmF, for simplification in further text the term Transformation, Research, Architecture, Development framework or TRADf will be used). As shown in Figure 1, Project resources interact with all the enterprise’s (or simply an Entity) architecture phases, using the data Building Blocks for Projects (dBB4P) or the holistic brick (Trad & Kalpić, 2020a). GPI is MLI4P’s main interface and the trends of using MLI4P for 2021, is tremendous, as shown in Figure 2 (Kapoor, 2021).

Figure 2.

The growing role of MLI4P on Hyperautomation (Kapoor, 2021)

Background

MLI4P uses the GPI to interact with the EAI4P and has the following characteristics:

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  Is an AI composite model, or set of algorithms, which can be integrated in various Projects.

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  Uses the atomic Building Blocks for Projects (aBB4P) concept; which corresponds to an autonomous set of classes.

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  Uses a Natural Programming Language for Projects (NLP4P) for development of various types of interfaces.

The author’s global research topic's and final Research Question (RQ) (hypothesis #1-1) is: “Which business transformation manager’s characteristics and which type of support should be assured for the implementation phase of a business transformation project?” The targeted business domain is any business environment that uses: 1) complex technologies; and 2) frequent transformation iterations. For this phase of research, the sub-question (or hypothesis #2-3) is: “What is the impact of the MLI4P on Projects?”