Artificial Intelligence and Big Data Analytics for Intelligent Digital Transformation Across Multiple Industries

Abstract

AI and big data analytics are some of the most disruptive technologies that have changed how many industries operate in essential ways within the digital era. Organizations are now presented with unprecedented opportunities to exploit intelligent decision-making systems due to the rapid exponential growth of digital data being generated via Internet of Things (IoT) devices, cloud computing platforms, enterprise information systems, social media and mobile application technology, along with industrial automation. AI, including machine learning, deep learning and natural language processing, is extensively used to generate action-oriented knowledge from the huge structured and unstructured data with great enabling power. More specifically, Big Data Analytics offers scalable computational frameworks to analyze large-scale datasets characterized by higher volume, velocity, variety and veracity; therefore providing solutions that support predictive analytics, privacy-preserving data mining (PPDM) project infrastructure in the premises of operational intelligence and strategic planning. AI and Big Data Analytics: AI converges with big data analytics to enable intelligent digital transformation by infusing automated Learning and real-time, predictive modeling of raw data in enterprise operations with autonomous decision support. More and more organizations across healthcare, manufacturing, finance (including insurance), transportation services, both within industry segments in addition to edge applications that span education, skills development, agriculture, energy, retail, telecommunications, logistics and public administration increasingly depend on these intelligent technologies to improve operational efficiency, customer satisfaction, asset utilization, sustainability, cyber resilience, business productivity and competitiveness. This paper provides a systematic review of Artificial Intelligence-powered Big Data Analytics for smart digital transformation in multi-sector industries. We contend that the proposed architecture can be utilized to support enterprise-wide digitization through an integrated framework of heterogeneous data acquisition, cloud-edge computing infrastructure, and distributed storage solutions. Intelligent feature engineering and machine learning model development using deep learning optimization and explainable AI mechanisms for real-time decision support. Thus, to provide a solution for the continuously evolving needs of modern organizations, it stresses scalable data integration, learning capabilities via adaptive systems and automated knowledge discovery with predictive intelligence, along with continuous feedback optimization resulting in fast-paced organizational evolution. In addition, it fosters data governance and includes security measures to protect users' information privacy as well as trust in artificial intelligence through ethical barriers and regulatory mechanisms. It also explores the impact of artificial intelligence (AI) powered predictive analytics on supply chain management, predictive maintenance, fraud and anomaly detection, personalized customer services, medical diagnosis, smart manufacturing, intelligent transportation, precision agriculture, energy optimization, financial risk assessment, etc. It must be stated that system performance evaluation shows a significant improvement in prediction accuracy and operational efficiency with the use of AI along with Big Data Analytics, as it not only reduces computational latency but also improves decision-making precision by reducing wastage of resources while accepting limitations shown across them. Experimental evidence suggests large increases in analytical accuracy, response times and scalability, as well as productivity gains in business intelligence generation between conventional data processing approaches versus the newer standards of digital transformation happening. In addition, the study also highlights contemporary technological difficulties such as data heterogeneity and distributional shifts in algorithms - as well as discussing important challenges of scalability-interoperability [15], privacy preservation-cybersecurity threats-intentional induction attack-explainable AI model-ethical issues-computational complexity-costs-and skilled workforce limitations. Emerging applications: Emerging technologies for intelligent enterprise ecosystems around federated Learning, edge intelligence, digital twins, and blockchain-enabled data governance need to be identified in order to gain advantage from the adoption of explainable AI; autonomous analytics (insights) generation using explanations and generative AI. Overall, the proposed framework shows how Artificial Intelligence and Big Data Analytics can link to intelligent adaptive security-based sustainable digital transformation strategies that facilitate organizational long-term innovation-competitive advantage across different industrial domains. The results are useful for researchers, industry practitioners, policymakers and technology developers who aim to execute large-scale AI-based digital transformation projects that respond flexibly to the fluctuating business environment while promoting economic growth, operational effectiveness and sustainable industrial ecology.