Using a Generic Artificial Intelligence Pattern for Business Transformation
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Using a Generic Artificial Intelligence Pattern for Business Transformation

Antoine Trad (IBISTM, France)
Pages: 300
DOI: 10.4018/978-1-7998-7214-6
ISBN13: 9781799872146|ISBN10: 1799872149|EISBN13: 9781799872160|ISBN13 Softcover: 9781799872153
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Description & Coverage
Description:

This book presents a cross-business domain Generic Artificial Intelligence Pattern (GAIP) that is the result of a lifetime of research on business (and societal) transformations, artificial intelligence (and algorithmics), applied mathematics, software modelling, business engineering, financial analysis and global enterprise architecture. The proposed GAIP formalism tries to imitate the human brain, an empirical process that is mainly based on the beam-search heuristic decision making process; processes that include Big Data management and concepts like machine learning. The proposed GAIP can be used to implement a complex decision-making system that can integrate in the enterprise’s business information and communication technology environments. The GAIP uses a behavior-driven development environment or a natural language programming that can be easily adopted by the development teams; where the Applied Holistic Mathematical Model (AHMM) subsystem offers such a high level implementation environment that can be used by any team member without any prior computer sciences schooling. The GAIP that can be used to model avant-garde enterprise architecture blueprints, business transformation projects or decision-making systems, is supported by many real-life cases of various business domains.

The uniqueness of this research is that the GAIP promotes a holistic AI global strategy, unbundling and the alignment of various enterprise business and architecture standards and strategies to support business transformation processes. This research is of particular importance for business managers, analysts, project engineers, and business environment’s architects, as well as researchers, students, and academicians.

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