The Integration Of Artificial Intelligence (AI) In Business Operations

Abstract

The steady rise of AI, especially in the finance, healthcare, and retail sectors, which has led to improved performance, has not been without problems and challenges. This paper reviewed 27 research papers using bibliometric and content analysis.

The review examined the complex and diverse effects of AI on various business aspects, including efficiency and decision-making, consumer experience, innovation, and performance. It has provided helpful information to businesses navigating the intricate terrain of AI integration through a comprehensive analysis of barriers and proposed solutions.

The problems associated with AI adoption were identified as data complexity, bias, ethical issues, human-machine collaboration, interpretability, and the lack of standardised evaluation metrics. Solutions for these problems have been listed. These solutions included frameworks for data governance, ethical AI design, frameworks for human-AI collaboration, explainability and interpretability tools, and contextualised evaluation metrics.

The methodological limitations of some papers do not allow for the generalisability of their findings.

This review also indicated the scope for future research. Further research into the capacity of AI integration in smaller businesses and startups may reveal inclusivity measures. Researching AI integration in developing industries and its impact on sustainability may be useful. Long-term studies on AI integration's social effects and AI-powered decision support tools for corporate executives could help us comprehend AI's transformative potential.