AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
Smitha Rajagopal, R. Karchi, Sanjeevakumar M.Hatture et al.· Journal of Intelligent Decis...· 0 citations
Artificial intelligence and computer science will converge to produce the next set of emerging technologies. Artificial intelligence enables machines to learn, make intelligent decisions, and adapt, but it is built on the foundations of computer science, including algorithms, data structures, optimization, complexity theory, and computing platforms. This theoretical review examines the role of these foundations in modern artificial intelligence systems and their contributions to the development of scalable, explainable, efficient, and deployable technologies. The paper discusses the technical foundations of machine learning, deep learning, graph-based intelligence, neuro-symbolic systems, explainable artificial intelligence, and edge intelligence in terms of algorithmic reasoning, data structures, learning optimization, computational efficiency, and computational infrastructure. It also discusses the role of integrating artificial intelligence and computer science across major application areas, including smart health, cybersecurity, robotics, natural language processing, Internet of Things systems, edge computing, and sustainable digital infrastructure. To improve the paper's conceptual framework, it proposes an Algorithm-to-Intelligence Integration Framework that connects computer science foundations, artificial intelligence paradigms, system requirements, application domains, and future technologies. The survey finds that intelligent systems should, in the future, combine adaptive learning with robust computational design to achieve responsible, secure, sustainable, and deployable technological advancement.
Smitha Rajagopal, S. Nirkhi, Bharati S. Pochal et al.· Future Technology· 0 citations