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Actual archaic Business Transformation Projects (BTP) are managed as separate black-boxes that are isolated silos where their internal and external components create a messy hairball that is called the enterprise’s Information and Communication System (ICS) (Desmond, 2013). As already mentioned, the AHMM is based on many real-life cases and uses a model that can be used in a large variety of application fields, like: 1) business transformation projects; 2) business engineering projects; 3) critical success factors management; and 4) EA development procedures. This article recommends that the ICS’s Decision Making System (DMS) uses the AHMM instance to solve problems by offering a set of possible solutions in the form of architecture, managerial and technical recommendations or blueprints, for any type of business problem; by using a central qualitative method based on a beam search (heuristic tree) that uses quantitative methods at its nodes. The proposed AHMM’s implementation is very complex and needs a profound understanding of many fields. The DMS’ actions produce solutions, which have the form of technical and managerial recommendations, can be applied by the business environment’s architects, business managers, business analysts and project engineers to enforce the implementation of the transformation processes. A DMS is a multi-objective, multi-project, multi-factor (CSF) and BTP problem in the context of a complex implementation phase. The DMS attempts simultaneously to maximize the success rate (Felfel, Ayadi & Masmoudi, 2017). Such processes should surpass the business environment’s currently used usual DMs. The AHMM is a model first modelling environment that is supported by an applicable framework (IBM, 2001; Trad & Kalpić, 2018a; Trad & Kalpić, 2018b). This article’s background combines Knowledge Management (KM), innovative decision making systems approach, enterprise architecture, heuristics/mathematical models, information technology management, business transformation initiatives and business engineering fields (Goikoetxea, 2004; Tidd & Bessant, 2009). As shown in Figure 1, where the major strategic technology trend is artificial intelligence based systems; so the authors conclude that building an innovative AHMM model (Cearley, Walker & Burke, 2016; Thomas, 2015; Ho, Xu & Dey, 2010). The AHMM model enables the implementation of a generic and cross-functional reasoning engine that is mainly based on: 1) factors classification and management mechanism; 2) an adapted qualitative heuristics tree (beam search) research method; and 3) a set of quantitative modules that can be triggered from the tree’s nodes. The AHMM manages sets of factors which can be applied to BTP or to any other type of project. This article’s authors based their research model mainly on intelligent neural networks which can execute specific calls to quantitative modules and is supported by information technology driven development models, where both disciplines, applied mathematics and information technology models are complementary, due to the use of many existing industry standards, like for example the Architecture Development Method (ADM) (The Open Group, 2011a; Tidd & Bessant, 2009). The AHMM holistic concept is mainly business driven and is agnostic to a specific business environment’s internals. As shown in Figure 1, it has been decided by the authors that this genuine research framework should be founded on DMS microartefacts that in turn are based on existing standards (Johnson & Onwuegbuzie, 2004).
Figure 1. Technology Trends (Cearley, Walker & Burke, 2016)