American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence for Intelligent Decision-Making in Business and Industry

Author(s) Maria Garlock
Country United States
Abstract Artificial Intelligence (AI) has become a transformative force in modern business and industrial environments by enabling organisations to make faster, more accurate, and data-driven decisions. Traditional decision-making methods often rely on historical analysis, human intuition, and manual processing, which may be inadequate for addressing the complexity, scale, and speed of today's dynamic business environment. The integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Big Data Analytics, Business Intelligence (BI), Cloud Computing, Internet of Things (IoT), Digital Twin Technology, Robotic Process Automation (RPA), Blockchain, Edge Computing, and Explainable Artificial Intelligence (XAI) is transforming organisational decision-making across finance, manufacturing, healthcare, logistics, retail, human resource management, and strategic planning.
This study presents a comprehensive analysis of Artificial Intelligence for Intelligent Decision-Making in Business and Industry. A qualitative analytical research methodology based on secondary data is employed to investigate AI-enabled decision support systems, predictive analytics, intelligent automation, business process optimisation, industrial intelligence, and multidisciplinary applications. The research evaluates how AI enhances strategic planning, operational efficiency, customer relationship management, supply chain optimisation, financial forecasting, risk management, and sustainable business performance.
The findings indicate that AI-driven decision-making significantly improves organisational productivity, forecasting accuracy, customer satisfaction, operational resilience, fraud detection, resource allocation, and innovation capability. Machine Learning algorithms identify hidden patterns within large datasets, while Deep Learning models support image recognition, predictive maintenance, and demand forecasting. Natural Language Processing enables intelligent customer service and document analysis, whereas Explainable AI improves transparency and trust in automated decisions.
Despite these opportunities, challenges remain concerning data quality, algorithmic bias, cybersecurity, privacy protection, workforce transformation, regulatory compliance, ethical AI implementation, and integration with legacy information systems. Future research should investigate autonomous enterprise intelligence, federated AI for collaborative decision-making, quantum-enhanced business optimisation, human–AI collaboration frameworks, and AI governance models for responsible organisational decision-making.
The study concludes that Artificial Intelligence provides a strategic foundation for intelligent business and industrial decision-making by integrating advanced analytics, automation, and human expertise to achieve sustainable organisational growth and competitive advantage.
Keywords Artificial Intelligence, Intelligent Decision-Making, Business Intelligence, Machine Learning, Predictive Analytics, Decision Support Systems, Industry 5.0, Explainable AI, Business Analytics, Digital Transformation.
Field Engineering
Published In Volume 2, Issue 4, July-August 2020
Published On 2020-08-16
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