Analyzing the Critical Parameters for Implementing Sustainable AI Cloud System in an IT Industry Using AHP-ISM-MICMAC Integrated Hybrid MCDM Model

Abstract

This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

“…Furthermore, uncertainty and ambiguity in project data, along with the dynamic nature of construction, complicate AI's implementation in cost management. To address these challenges, this research proposes a novel methodology: the fuzzy integrated Delphi-ISM-MICMAC hybrid multi-criteria approach [3]. This approach combines fuzzy sets, the Delphi method, ISM, and MICMAC to optimize the AI factors in civil engineering cost management.…”

“…This research aims to advance MCDM knowledge and create innovative methodologies to tackle the real-world challenges in civil engineering. By integrating these MCDM tools, this study provides a structured framework to identify, evaluate, and prioritize the AI factors relevant to cost management in civil engineering [3,4]. This research takes a holistic approach that considers the interdependencies and dynamic interactions between AI factors, aiming to offer insights into using AI technologies strategically for cost optimization in civil engineering projects.…”

“…By linking theory with practical application, it offers a transformative framework poised to redefine cost management in the civil engineering sector. This study represents a pioneering effort to revolutionize cost management practices through a fuzzy integrated Delphi-ISM-MICMAC hybrid multi-criteria approach [2,3]. The research addresses the urgent need for improved cost optimization strategies in civil engineering projects.…”

“…Natural language processing (NLP) can extract and interpret regulatory information, allowing organizations to proactively identify compliance gaps and take corrective action. AI-based compliance management systems enhance transparency, accountability, and regulatory oversight in civil engineering projects [3,5,9,23,24].…”

“…Compliance monitoring (F62): Compliance monitoring ensures adherence to regulatory standards, reducing legal risks and preventing penalties, thereby maintaining budget integrity. It allows for the timely identification and resolution of non-compliance issues, promoting project success while upholding legal and ethical standards [3,24,48,49].…”