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.

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .

University-industry collaboration plays a vital role in nation's innovation system. This study presents an empirical analysis of R&D collaboration between university and industry. The literature focused on the all those factors including firm size, firm's innovation activity and openness of the firm affecting university-industry collaboration. Primary data is used and sample contains 15 industrial sectors of Pakistan according to market capitalization at the time of data collection. The empirical results of the study suggest that firm's size, number of employees and openness of the firm have positive impact on uni-industry collaboration for R&D projects. Whilst annual budget of the firms is found to have negative relationship with R&D collaborations and larger firms are found to be less efficient in taking advantages of R&D collaboration with universities because larger firms have more spending on their fixed costs as they have participated in so many R&D activities.