Future of Business Culture: An Artificial Intelligence-Driven Digital Framework for Organization Decision-Making Process

Technological efforts are currently being used across a broad array of industries. Through the combination of consumer choice and matching principle, the goal of this paper is to investigate the prospective implications of artificial intelligence systems on businesses' outcomes. From an entrepreneurship standpoint, the research revealed that artificial intelligence systems can help with better decision-making. What impact does the introduction of AI-based decision-making technologies have on organizational policymaking? The quirks of human and AI-based policymaking are identified in this research based on five important contextual factors: precision of the choice search area, contribution to the innovation of the policymaking process and result, volume of the replacement collection, policymaking pace, and generalizability. We create a novel paradigm comparative analysis of conventional and automation judgment along these criteria, demonstrating how both judgment modalities can be used to improve organizational judgment efficiency. Furthermore, the research shows that, by involving internal stakeholders, they can manage the correlation among AI technologies and improve decision for businessmen. Furthermore, the research shows that customer preferences and industry norms can moderate the link between AI systems and superior entrepreneurial judgment. The goal of this work is to conduct a thorough literature analysis examining the confluence of AI and marketing philosophy, as well as construct a theoretical model that incorporates concerns based on established studies in the areas. This research shows that, in a setting with artificial intelligence systems, customer expectation, industry standards, and participative management, entrepreneurial strategic decisions are enhanced. This research provides entrepreneurs with technology means for enhancing decision-making, illustrating the limitless possibilities given by AI systems. A conceptual approach is also formed, which discusses the four factors of profit maximization: relationship of AI tools and IT with corporate objectives; AI, organizational learning, and decision-making methodology; and AI, service development, and value. This study proposes a way to exploit this innovative innovation without destroying society. We show real-world examples of each of these frameworks, indicate circumstances in which they are likely to improve decision-making performance in organizations, and provide actionable implications into their constraints. These observations have a wide variety of implications for establishing new management methods and practices from both academic and conceptual viewpoints.